research on the relationship between mental health and academic achievement

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Published: 27 February 2024

Adolescent mental health and academic performance: determining evidence-based associations and informing approaches to support in educational settings

Xzania Lee 1 ,
Anya Griffin 1 , 2 ,
Maya I. Ragavan 3 &
Mona Patel 1 , 2

Pediatric Research volume 95 , pages 1395–1397 ( 2024 ) Cite this article

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In 2021, the American Academy of Pediatrics, the American Academy of Child and Adolescent Psychiatry and the Children’s Hospital Association (CHA) declared a “National State of Emergency in Children’s Mental Health.” 1 The statement identified how the pandemic exacerbated the already worsening mental health problem among US youth due to the compounding challenges faced by youth and acknowledged the significant impact of this mental health crisis on youth. This declaration made a call for schools, policymakers, and advocates for children and adolescents to prioritize and focus on pediatric mental health.

Adolescent mental health and academic performance are intricately linked aspects of development, each influencing and being influenced by the other. The recognition of this bidirectional association has sparked considerable interest within the research community, prompting an investigation into the nuanced dynamics between mental health and educational outcomes during the formative adolescent years. Numerous studies have explored the connection and influence between mental health and academic performance, and further acknowledge that the multifaceted interconnectedness of mental health and academic performance require a holistic view. 2 , 3 , 4 Researchers have identified that higher academic aspirations are associated with better mental health outcomes and that socioemotional well-being is needed for academic thriving. 5 , 6 Furthermore, Yu and associates described how interpersonal relationships are positively correlated with academic performance, especially student-peer relationships, which had more influence than the parent-student or teacher-student relationship on academic achievement. 7 , 8 Finally, impacts of social determinants of health have been shown to exert profound influences on both mental health and academic outcomes further emphasizing the need to consider the broader ecological context in which adolescents develop and the importance of considering a socioecological model, suggesting that factors such as family, school, and community environments play pivotal roles in shaping both mental health and academic outcomes. 6 , 9

In this article by Monzonis-Carda and associates, the authors explore the bidirectional longitudinal association between the dual-factor model of mental health and academic performance in adolescents. The dual-factor model of mental health, in contrast to traditional models of mental health which focus on psychopathological symptoms, integrates mental health wellbeing and psychopathology into a mental health continuum. 10 The authors hypothesize that a bidirectional association between academic performance and adolescent mental health would be present in their sample of 266 secondary school students from Spain. They assessed mental health through the Spanish language Behavior Assessment System for Children and Adolescents (BASC-S3) and examined grade point average, and academic performance based on the Test of Educational Abilities. They then employed a cross-lagged modeling approach to analyze the bidirectional association over 2 years. The key findings suggested that higher academic performance at baseline was associated with better mental health over time, but better mental health was not associated with academic performance. Therefore, the association was not bidirectional as expected. Based on these findings, the authors posit academic performance may be a predictor of adolescents’ mental health status; and conversely, mental health may not be a predictor of adolescents’ academic performance. They offered school-based recommendations for the promotion of good mental health practices for students with low academic performance and supported future policy and health and educational professionals to promote adolescent mental health wellbeing. Overall, the article underscores the importance of considering academic performance as a target for interventions to promote adolescents’ mental health. It suggests that focusing on reducing school pressure and establishing personalized academic goals could contribute to better psychological well-being.

While the article provides some important insights into the association between mental health and academic performance in adolescents, some limitations were noted. While there is some limited adjustment for socioeconomic status, the article lacks a comprehensive exploration of social determinants of health and impacts of adverse childhood events (ACES), such as cultural background, and other important social and familial dynamics. These factors play a pivotal role in shaping an adolescent’s mental health and academic performance and may result in an oversimplified understanding of the complex interplay between mental health and academic outcomes. The study further focuses on academic grades and “abilities” as indicators of academic performance. Academic success is multifaceted and includes factors like motivation, engagement, and teacher-student relationships, and a more nuanced exploration of these components could provide a richer understanding of the relationship between mental health and academic outcomes. The study authors reviewed limitations that require further investigation including the use of BASC-S3 as the primary self-reported measure of adolescent mental health. Depending on individual developmental level of insight and situational context, adolescents are often unreliable and inaccurate reporters of their functioning, and adolescents in clinical populations tend to overreport symptoms and provide inaccurate information regarding their functioning on the BASC-S3. 11 , 12 Incorporating objective measures or multi-method assessments and the inclusion of multi-rater methods (i.e., teachers, caregivers, etc.) may provide a more detailed picture of the student’s true socioemotional functioning through the provision of differing perspectives of each student’s functioning. 13 The study’s authors also acknowledge a relatively small sample size, homogeneity of the study population, and short study length to determine longitudinal outcomes may further limit generalizability to other populations. Lack of testing for sex assigned at birth and self-identified gender effects, and not integrating broader social determinant impact upon adolescent mental health may result in misguided or ineffective approaches to promoting mental health in adolescents. Further, previous research and psychological assessment literature have indicated the significant impact of social determinants of health and ACES on youth academic achievement and behavioral health outcomes. Students with elevated social risk, including ACES, are often at increased risk for mental health and academic achievement deterioration. 14 This supports the need for school leaders and policymakers to continue to focus efforts on maximizing the recognition of these factors for youth and promote the implementation of programs to address roots of social risk and integration of socioemotional mental health supports in academic institutions. 15 Due to the interconnectedness of mental wellness and academic success, addressing aspects of mental health functioning within the school setting will equip students with the essential skills to navigate challenges, manage stress, and build resilience. By bolstering emotional, behavioral, and social skills, students are primed to engage in learning, establish positive relationships with peers and teachers, and cope with the pressures of academic stress and daily life hassles. 16 A structured educational tier one (i.e., general education curriculum) mental health intervention will assist students with stress reduction, and behavior management, improve executive functioning skills, and establish a scholastic environment conducive to effective knowledge consumption and academic performance. 17 Incorporating evidence-based practices to support student emotional wellness holistically nurtures the development of students and provides a foundation for lifelong well-being and academic excellence. While this article contributes to the understanding of the association between mental health and academic performance, it also highlights the need for future exploration of factors that influence the causality between adolescent mental health and academic performance and further informs the recommendation to have mental health interventions and social-emotional learning curriculums in educational settings.

The 2021 joint declaration of the “National State of Emergency in Children’s Mental Health” catalyzed federal, state, and local awareness of evolving needs in pediatric mental health in the United States of America. While there has been increasing bipartisan support and focus for mental health funding at all levels of government, appropriate allocation of such funding to support identifying factors that impact pediatric mental health and using a data-driven approach to effective programming is critical. An example of more recent federally supported funding programs for child mental health includes the Health Resources & Services Administration (HRSA) funded Pediatric Mental Health Care Access (PMHCA) program, which has seen increased funding from 2018 through 2022, with an additional 80 million dollars added by the Bipartisan Safer Communities Act. ( https://mchb.hrsa.gov/programs-impact/programs/pediatric-mental-health-care-access ) Currently, under-resourced and under-reimbursed health systems fraught with post-pandemic short staffing and pre-pandemic existing behavioral health access challenges pose continued roadblocks to access. Pediatric policy recommendations to aid with improving meaningful pediatric mental health access include:

Increased funding and support for access to meaningful mental health resources in the community and schools

Integrated behavioral health delivery models within primary care and specialty care will be critical in enhancing access to care.

Increase the behavioral health workforce, training programs for primary care pediatricians and pediatric psychologists are needed, as the number of child psychiatrists and pediatric psychologists is currently not sufficient to meet demand.

Innovative and integrative team-based models including non-traditional licensed and non-licensed behavioral health support teams, including community health work may allow further access and a more impactful peer-to-peer support structure.

Behavioral health reimbursement shifts may ultimately be required to build infrastructure to address the current critical socio-emotional needs of our youth. Ultimately, research informing a more comprehensive perspective, including health-related social needs and ACES will be essential for advancing the field with evidence-based mental health interventions for youth.

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Lee, X., Griffin, A., Ragavan, M.I. et al. Adolescent mental health and academic performance: determining evidence-based associations and informing approaches to support in educational settings. Pediatr Res 95 , 1395–1397 (2024). https://doi.org/10.1038/s41390-024-03098-3

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Home > Books > Health and Academic Achievement - New Findings

Relation between Student Mental Health and Academic Achievement Revisited: A Meta-Analysis

Submitted: 15 June 2020 Reviewed: 24 December 2020 Published: 20 January 2021

DOI: 10.5772/intechopen.95766

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In the present research, the relationship between mental health and academic achievement in adolescents was investigated. The research adopted meta-analysis model to investigate the relationship between these two phenomena. In the meta-analysis, 13 independent studies were included, and their data were combined to display effect sizes. According to the result of the research, it was indicated that there was a positive relationship between mental health and academic achievement. Also, it was revealed that there was no significant relationship within sub-group variation in the relationship between mental health and academic achievement in terms of year of publication, publication type, community, and sample size, but not the setting.

mental health
academic achievement
mental health in adolescents
meta-analysis

Author Information

Gokhan bas *.

Department of Curriculum and Instruction, Faculty of Education, Nigde Omer Halisdemir University, 51100, Merkez, Nigde, Turkey

*Address all correspondence to: [email protected]

1. Introduction

In recent years, mental health of adolescents has taken considerable attention worldwide, because of a dramatic upward trend in suicide [ 1 ]. More than twenty percent of adolescents in the U.S. have a mental health disorder [ 2 ], and one in five of them are affected by a mental health problem [ 3 ], which is estimated to account for a larger burden of disease than any other class of health conditions [ 4 ].

The mental health field has traditionally focused on psychological ill-health, such as symptoms of anxiety or depression [ 5 ]. The most common mental health disorders among adolescents include obsessive–compulsive disorder, attention deficit hyperactive disorder, bi-polar disorder, impulse disorders, and oppositional defiance disorder [ 6 ]. Often, adolescents experience mental health problems, with fewer than half of them [ 7 ], in other words nearly one third of them need receiving treatment [ 8 ]. The situation is much more severe in adolescents living in racial and ethnic communities, who are more likely to have mental health problems [ 9 ]. Moreover, evidence suggests that adolescents coming from such communities are less likely to use mental health services, compared adolescents living in non-racial and ethnic communities [ 10 ]. Thus, when adolescents struggle with mental health problems, they often have attendance problems, difficulty completing assignments, increased conflicts with adults and peers [ 11 ]. Also, mental health problems adolescents have negatively impact their academic productivity and interpersonal relationships [ 12 ], and as a result of such problems, one million of adolescents – which is deemed to be very high – drop out of school annually in the U.S., for example [ 13 ].

Mental health issues among adolescents not only cause such problems, but they also negatively influence schooling [ 14 ]. Adolescents with mental health problems are at risk for schooling [ 15 ], and they may have increased difficulties primarily with academic achievement in school [ 16 ]. Frequent feelings of mental health problems exhibit school difficulties, including poor academic achievement [ 17 ]. Adolescents displaying strong mental health are likely to have better academic achievement, compared to adolescents displaying weak mental health [ 18 ]. Adolescents showing strong mental health have good social skills with both adults and peers [ 19 ], and their enhanced social and emotional behaviors have a strong impact on academic achievement [ 20 ]. Therefore, mental health problems in adolescents may have an important influence on academic achievement, which in turn have lifelong consequences for employment, income, and other outcomes [ 21 ]. Mental health issues may become problematic for adolescents in that they negatively influence academic achievement [ 22 ], which also might affect their future employment, health, and socioeconomic status [ 23 ].

Mental health problems of adolescents have an important influence on their schooling, particularly their academic achievement, which in turn may create important lifelong consequences. Due to a growing interest in mental health of adolescents in recent years, a meta-analysis seems timely, not only to demonstrate the association between mental health and academic achievement, but also to identify moderators that should be articulated in more depth in future research. Although there is a body of research on the relationship between mental health and academic achievement across the world, the literature is missing a meta-analysis of this relationship. To date, no meta-analytic research has examined the potential relationship between mental health and academic achievement, and the present research aims to fill this gap in the scope. Thus, the present research attempts to synthesize this association between mental health and academic achievement of adolescents. This meta-analysis aimed to answer the following research questions: (a) What is the relationship between mental health and academic achievement? (b) Does this relationship depend on year of publication? (c) Does this relationship depend on setting? (d) Does this relationship depend on community? (e) Does this relationship depend on sample size?

2. Methodology

The present research adopted meta-analysis model [ 24 ] to combine data from independent studies to draw a single conclusion with greater statistical power [ 25 ]. Meta-analysis is a model that reviews the research results and combines the data obtained from independent studies in statistical ways [ 26 ].

2.2 Data sources

Research examining the relationship between mental health and academic achievement was identified through a search of reference databases. To identify relevant empirical research on the relationship between mental health and academic achievement, a systematic literature review was conducted over a two-month time for the period 2000 to 2020, using such databases as Education Resources Information Center (ERIC), PsycINFO, Web of Science, EBSCOhost, Science Direct, Scopus, ProQuest®, and Google Scholar, with the following queries: [(“mental health” OR “mental health disorders”) AND (“mental health and academic achievement” OR “mental health disorders and academic achievement”], [“academic achievement” AND “academic success”], [(“adolescents mental disorders” OR “adolescents mental health”) AND (“adolescents mental health academic achievement” OR “adolescents mental health disorders academic success”)]. As a result of such review, a total of 52 studies including 34 journal articles and 18 postgraduate dissertations were reached. Thus, over 50 potential independent studies were generated for preliminary review as a result of the literature search.

2.3 Inclusion criteria

To be eligible for inclusion in the present meta-analysis, a study had to (a) investigate the relationship between mental health and academic achievement; (b) include studies conducted on adolescents; (c) have taken place from 2000 to the present; (d) be reported to be available in English; and (e) include sample size and correlation coefficients.

The first four criteria were used in an initial screening of the abstracts of the studies. If the study had no abstract available, the full publication was collected and examined thoroughly. For the last criterion, the full publication was examined, and it was checked whether it included sample size as well as correlation coefficients. For the studies with insufficient statistical information, the corresponding author was contacted and the relevant information for the missing data was requested. If the author did not respond or could not provide the missing data, the study was excluded from the meta-analysis. After checking each study in the light of the inclusion criteria, the author agreed that 13 studies met all the five criteria of the research (see Table 1 ).

Author(s)	Publication type	Setting	Community	Sample size
White [ ]	Dissertation	U.S.	Urban	780
Sathiyaraj and Babu [ ]	Journal article	Non-U.S. (India)	Combination	750
Chung [ ]	Dissertation	Non-U.S. (Australia)	Urban	261
Eisenberg et al. [ ]	Journal article	U.S.	Urban	2.798
Gilavand and Shooriabi [ ]	Journal article	Non-U.S. (Iran)	Combination	200
Mundia [ ]	Journal article	Non-U.S. (Brunei)	Urban	6
Geetha [ ]	Dissertation	Non-U.S. (India)	Combination	1.088
Jenkins [ ]	Dissertation	U.S.	Urban	331
Singh [ ]	Journal article	Non-U.S. (India)	Urban	200
Sheykhjan et al. [ ]	Journal article	Non-U.S. (Iran)	Urban	314
Murphy et al. [ ]	Journal article	Non-U.S. (Chile)	Urban	37.397
Talawar and Das [ ]	Journal article	Non-U.S. (India)	Combination	200
Manchri et al. [ ]	Journal article	Non-U.S. (India)	Urban	270

Studies included in the meta-analysis.

In order to investigate possible relationship between mental health and academic achievement, five moderators were extracted from the studies [ 40 ]. The first moderator concerned with the year of publication. The year of the publications were classified as 2009–2014 and 2015–2020, with a range of five years. The second moderator, publication type, referred to whether a study appeared as a journal article or a postgraduate dissertation. The third moderator, setting, referred to the country in which the research was conducted. Because the studies included in the meta-analysis were not from diverse settings – they were mainly coming from the U.S. and some Asian countries including India and Iran – the setting was classified as U.S. and non-U.S. The fourth moderator of the research, community, referred to the society people are living in. Because there was no study only conducted in rural settings, the community included urban and combination (urban, suburban, and rural). The last moderator, sample size, was classified as 1–500 and 501 above.

2.4 Computation of effect sizes

Standard procedures for conducting meta-analyses were followed [ 41 ], and the correlation between mental health and academic achievement were examined though effect sizes of independent studies. The effect size obtained in meta-analysis is a standard measure value used to determine the strength and direction of the relationship in the research [ 42 ]. In meta-analytic research, the variance depends strongly on correlation coefficient [ 43 ]. Pearson’s correlation coefficient ( r ) was calculated as effect size in the present research. For this reason, correlation coefficients were transformed into Fisher’s z coefficient for computing the effect sizes, and analyses were conducted through the transformed coefficients [ 44 ]. In meta-analysis research, when the variable consists of more than one factor and when more than one correlation value is given, there are two different approaches about which one of them can be used [ 45 ]. In this research, if the correlations were independent, all relevant correlations were included in the analysis and accepted as independent studies. When dependent correlations were given, the correlations were averaged.

There are two basic models in meta-analysis research; they are fixed effects and random effects models. When deciding which model to use, it is necessary to look at which model’s prerequisites are met by the features of the studies included in meta-analysis [ 46 ]. The fixed effect model is based on the assumption that when the data obtained are homogeneous, all the collected studies estimate exactly the same effect [ 47 ]. In this model, it is thought that the variance among the study results is caused by the data related to each other [ 48 ]. According to the fixed effect model, there is one effect size shared by the studies showing the same effect size for all studies [ 49 ]. In cases where the studies included in the meta-analysis show heterogeneous characteristics, it is more appropriate to use the random effect model [ 50 ]. This model is used in cases where the data obtained are not homogeneous [ 51 ]. As a result, while deciding which statistical model to use during meta-analysis, it should be tested whether the effect sizes show a homogeneous distribution.

In addition, the coefficient classification is taken into account in the interpretation of the effect sizes obtained as a result of meta-analysis [ 52 ]. In this research, Cohen’s [ 53 ] effect size classification was taken into account in the interpretation of effect sizes. According to this classification, values between .20 and .50 correspond to small effect size; values between .50 and .80 correspond to medium effect size; and values above .80 correspond to large effect size.

2.5 Publication bias

Publication bias refers to the possibility that all studies performed on a particular subject will not be representative of the reported studies [ 54 ]. Since the studies where statistically significant relationships are not determined or studies with low level relationships are not deemed worthy to be published, this affects the total effect size negatively and increases the average effect size bias [ 55 ]. So, effect sizes seem to be higher than what they normally are [ 56 ].

A number of calculations are used to reveal publication bias in meta-analysis research, including methods such as funnel plot, classical fail-safe N , Orwin’s fail-safe N , and Duval and Tweedie’s trim and fill. The first method used to determine whether studies have publication bias is funnel plot [ 57 ]. The funnel plot, which displays the possibility of a publication bias in meta-analysis research [ 58 ], created for the relationships between mental health and academic achievement was shown in Figure 1 .

research on the relationship between mental health and academic achievement

Funnel plot for the effect size of the relationship between mental health and academic achievement.

The funnel plot is expected to be significantly asymmetrical in publication bias. In cases where publication bias is not observed on the funnel plot, the effect sizes are symmetrically scattered around the vertical line. The line in the middle of the funnel plot shows the overall effect, and individual studies are expected to cluster around this line [ 59 ]. Studies which are asymmetrically scattered around the funnel plot refer to a possible publication bias in meta-analysis [ 60 ].

Also, classical fail-safe N was performed to reduce the average effect size to insignificant levels which is needed to increase the p -value for the meta-analysis to above .05 [ 61 ]. Classical fail-safe N showed that a total of 1699 studies with null results would be required to bring the overall effect size to trivial level at .01. Besides, Orwin’s fail-safe N was performed to decide the values of criterion for a trivial log odd’s ratio and mean log odds ratio in missing studies [ 62 ]. As a result of it, the number of missing null studies to bring the existing overall average effect sizes to trivial level at .01 was found to be .243.

Lastly, to assess the possibility of publication bias in the studies the trim and fill method, which is a nonparametric method of data augmentation used to estimate the number of studies absent from a meta-analysis due to the exclusion on one side of the funnel plot of the most extreme findings [ 63 ], was performed. With the help of this statistic, small studies at the far end on the positive side of the funnel plot are removed. The effect size is recalculated until the funnel plot is symmetrical [ 64 ]. When there is publication bias in the studies, the effect sizes are distributed asymmetrical on the funnel plot. In the research, the funnel plot provided evidence that there is no publication bias in the meta-analysis.

3.1 Overall results

A total of 13 studies were included in the meta-analysis with a sample size of 44.595 adolescents. As a result of the comparisons, the Q value indicated that the distribution of effect size of the studies was heterogeneous, Q (12) = 1002.815, p < .001, so that a random effects model was adopted in the meta-analysis (see Table 2 ).

Model				CI
Model				Lower	Upper
Overall	13	.334	.155	[.187	.467]				98.803
Year of publication 2009–2014 2015–2020 Publication type Journal article Dissertation	4 9 9 4	.256 .267 .256 .430	.034 .010 .010 .047	[.227 [.258 [.247 [.397	.285] .276} .265] .462]	.004 .005	1 1	.949 .942	.00 .00
Setting U.S. Non-U.S.	3 10	.107 .399	.232 .206	[−.126 [.212	.329] .557]	3.899	1	.048	.00
Community Urban Combination Sample size 1 ≤ N ≤ 500 501 ≤ N	9 4 8 5	.250 .532 .408 .260	.010 .051 .053 .010	[.241 [.502 [.369 [.251	.259] .562] .447] .268]	.990 .796	1 1	.320 .360	.00 .00

Results related to overall effect sizes of the studies.

Due to that they did not report in English, other studies coming from diverse settings across the world were not included.

Because there was no study only conducted in rural settings, the community included urban and combination (urban, suburban and rural).

Table 2 demonstrated the relationship between mental health and academic achievement of adolescents. The effect size of the relationship between mental health and academic achievement computed by random effects model was r = .334 (95% CI = .187–.467). The confidence interval showed that the true effect size was likely to fall in the .187 to .467, which indicated a low to medium effect [ 65 ]. The computed effect size revealed that there is a moderate level of positive correlation between mental health and academic achievement. The forest plot of the relationship between mental health and academic achievement was displayed in Figure 2 .

Forest plot of the relationship between mental health and academic achievement.

Moderator analyses were performed to examine whether the effect sizes were attributable to the basic research sub-groups. Results indicated that this was not the case, as neither sub-group, excluding the setting, moderated the research findings. There was no significant relationship within sub-group variation in the relationship between mental health and academic achievement in terms of year of publication Q b (1) = .004, p = ns , publication type Q b (1) = .005, p = ns , community Q b (1) = .990, p = ns , and sample size Q b (1) = .796, p = ns , but not the setting Q b (1) = 3.899, p = .048. In other words, no significant moderation effect was found, which means that the relationship between mental health and academic achievement does not depend on the basic sub-groups, excluding the setting.

4. Discussion

The present research quantitatively synthesized the results of 13 independent studies, conducted over the past two decades, which examined the relationship between mental health and academic achievement in adolescents. The results of the research confirmed that there is a significant positive relationship between mental health and academic achievement. These results are consistent with the recent research investigating the relationship between mental health and academic achievement [ 27 , 34 ]. Mental health problems may create many obstacles to adolescents, not just in their daily life routines, but also in their schooling academically. Mental health risks have long term and complex interactions with academic outcomes [ 27 ]. Mental health issues among adolescents not only cause pain and distress, but they also influence negatively their potential for success in school [ 14 ]. More and more adolescents – for example in the U.S. – face with mental health problems annually [ 1 ], and their behaviors lead to feelings of anxiety or depression [ 66 ]. The effects of mental health problems negatively influence the academic performance primarily [ 22 ], and as a result of it, more than one million adolescents drop out of school every year in the U. S. [ 23 ]. Mental health problems make adolescents face with a decline in academic achievement [ 67 ], which in turn results in school absence, poor grades, and even repeating a grade in school [ 68 ]. Those adolescents reporting high level of mental health problems are more likely to perceive themselves as less academically competent [ 69 ], and they display low academic achievement in school [ 70 ]. When schools identify problem behaviors with programs of intervention, it is likely to improve academic achievement of adolescents [ 71 ]. Well planned and well-implemented programs to foster mental health [ 72 ] can make adolescents achieve better academically in school [ 20 ]. However, in the U.S. – for example – 70 percent of adolescents who need mental health intervention cannot receive services [ 22 ], and nearly one third of them who need help receive treatment [ 8 ], which in turn negatively influences their academic achievement. Therefore, early detection of mental health problems of adolescents can have access to appropriate services which lead to improvement in both mental disorder symptoms and academic performance [ 73 ].

In addition to these overall findings, this meta-analysis also looked at the influence of some moderators in the association between mental health and academic achievement. It was revealed that no variables moderated the relationship between mental health and academic achievement, but not the setting. There was no significant relationship within sub-group variation in the relationship between mental health and academic achievement in terms of year of publication, publication type, community, and sample size.

First, year of publication did not appear to be a moderator in the association between mental health and academic achievement, indicating that the effect sizes of all studies included in the meta-analysis were similar. Second, the publications included in this meta-analysis were dissertations and journal articles. Although dissertations had a higher effect size compared to journal articles, publication type did not appear to be a moderator in the relationship between mental health and academic achievement. This showed that in spite of the fact that journal articles are selective to display significant results [ 74 ], they produced similar effect sizes as with dissertations which keep relatively minor results unpublished. Third, community did not appear to be a moderator in the association between mental health and academic achievement, which indicated that studies conducted both in urban and combination societies had similar effect sizes. This result revealed that mental health of adolescents living in both urban and combination communities is associated positively with academic achievement. Also, sample size did not appear to be a moderator in the relationship between mental health and academic achievement. Studies including more than 500 adolescents did not contribute significantly to the effect sizes, which indicated that the association between mental health and academic achievement was not affected by sample size.

However, it was indicated that setting appeared to be a significant moderator in the association between mental health and academic achievement. This result showed that studies conducted in the U.S. and countries out of the U.S. impacted differently to overall effect size. According to this result, countries out of the U.S., which are mainly Asian contexts, had a high effect size in the relationship between mental health and academic achievement. It may be due that the U.S. has relatively more racial and ethnic communities, or immigrants, compared to other countries, and such diversity of the U.S. may have an influence on the result obtained in the research. In the U.S. 70 percent of the adolescents need mental health interventions [ 22 ]; however, the situation is much more severe in minority communities [ 9 ]. Adolescents living in racial and ethnic communities in the U.S. are less likely to use mental health services due to poverty in particular [ 10 ]. Poverty has a disproportionate effect on racial and ethnic minorities, and adolescents who live in such condition are more likely to have a mental disorder [ 9 ]. As a result, almost half of the adolescents living in ethnic and racial communities in the U.S. fail to graduate due to the low level of academic achievement in school [ 75 ].

Lastly, although the meta-analysis included the studies which took place from 2000 to the present, no study could be reached in 2020 probably due to the Covid-19 pandemic. Since the outbreak in Wuhan, China, nearly all countries across the world has faced with the Covid-19 pandemic in 2020. The pandemic has created severe consequences for millions of people in either losing their lives or their jobs. Many countries, including the U.S., the U.K., France, Germany, China, Italy, and Spain at the top, imposed lockdowns for several months and tried to prevent the fast spread of the virus. The pandemic not only affected general health of individuals and social lives of people, but it also impacted the schooling of many students. Most educational institutions around the world canceled in-person instruction and moved to distant teaching in an attempt to contain the spread of Covid-19 [ 76 ], and they are still pursuing this kind of teaching through digital platforms, such as Zoom, Skype, Google Meet, Microsoft Teams, and so on. Owing to the closure of schools, researchers have faced with considerable difficulty in reaching participants to conduct empirical studies; so this may have influenced the future research on the relationship between mental health and academic achievement in 2020.

On the other hand, the Covid-19 pandemic might have affected the mental health of adolescents worldwide because they were imposed curfew for several months at home. During the lockdown, millions of adolescents had to stay home, and they were in social isolation both from their peers and the society. Many countries implemented isolation policies for adolescents in particular, due to the fact that these individuals have the potential to spread the virus easily to relatively older people which may result in higher fatalities. Affected by the long months in lockdown, many adolescents had to spend their time at home and pursue their education through digital platforms. Many adolescents faced with severe difficulties in pursuing their education at home, as well as they had problems in access to treatment as a result of losing their mental health. Many students confined at home due to Covid-19 may have felt stressed and anxious, and this may negatively have affected their mental health [ 76 ]. Many adolescents, having mental health problems, have faced with severe academic difficulties and dropped out of school [ 77 ]. During the pandemic, the dropout rates in adolescents have substantially increased across the world, and this in turn may have affected their schooling negatively, particularly their academic achievement. However, there is no empirical evidence to support the relationship between mental health and academic achievement during the Covid-19 pandemic; therefore it is timely to conduct research to investigate this potential association to prevent mental health disorders in adolescents and improve their academic achievement. Although the present meta-analysis showed that there is a positive relationship between mental health and academic achievement in adolescents, this cannot be the case during the pandemic. Months of curfew and lockdown may have influenced the mental health and academic achievement of adolescents; so future research is needed to better clarify the relationship between these two phenomena.

5. Conclusion

The present meta-analysis aimed to determine the relationship between mental health and academic achievement in adolescents. This research, as expected, confirmed that there is a positive relationship between mental health and academic achievement. The research also indicated that mental health of adolescents is very important for schooling, in that it has a potential to influence academic achievement positively or negatively. Therefore, it is deemed crucial for adolescents to have a strong mental health to perform better academically in school, which in turn have lifelong consequences for employment, income, and other outcomes [ 21 ].

Results also indicated that there was no significant relationship within sub-group variation in the relationship between mental health and academic achievement in terms of year of publication, publication type, community, and sample size, but not the setting. It was indicated that setting appeared to be a significant moderator in the association between mental health and academic achievement. This result showed that studies conducted in the U.S. and countries out of the U.S. impacted differently to overall effect size. According to this result, countries out of the U.S. had a high effect size in the relationship between mental health and academic achievement. The effect size of the studies conducted in the U.S. was found to be relatively low, which implied that ethnic and racial diversity might have an impact on the result obtained in the research. This underlines the role of the school; thus, if schools identify mental health problems of adolescents with programs of intervention, it is likely to improve academic achievement [ 71 ]. Schools play an important role in determining the mental health of adolescents because they serve more than 95 percent of a country’s young people population [ 78 ].

A relatively small number of studies have been identified in the present meta-analysis, so more studies are needed to better clarify the relationship between mental health and academic achievement in adolescents. This research included only studies reported in English; therefore further meta-analyses might be conducted to include other reports out of English. Also, the role of school-based intervention programs in the relationship between mental health and academic achievement has not been taken into account in the present meta-analysis, so further research might be carried out to clarify the issue. The research has reported that school-based intervention programs may be effective to prevent mental health problems, and thus foster academic achievement [ 14 ]. In particular, adolescents living in ethnic and racial communities suffer from mental health problems, and academic achievement in school is influenced by such background. Because of this, mental health issues of adolescents living in ethnic and racial communities should be taken into consideration seriously.

Conflict of interest

The author has no conflicts of interest to declare.

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Relationship Between Achievement Motivation, Mental Health and Academic Success in University Students

Affiliations.

1 Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj, Iran.
2 Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran.
3 Kurdistan University of Medical Sciences, Sanandaj, Iran.
4 Spiritual Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran.
PMID: 34176355
DOI: 10.1177/0272684X211025932

Students of medical sciences are under intense mental stress induced by medical training system and are more likely to develop psychological and mental disorders. These psychological disorders may influence their performance in different aspects of life including their study. The aim of the present study is to assess the possible relationships between mental health, achievement motivation, and academic achievement and to study the effect of background factors on mentioned variables. The sample group consists of students of Kurdistan University of medical sciences. 430 students at Kurdistan University of Medical Sciences were selected randomly to participate in the present cross-sectional study in 2016. We used General Health Questionnaire (GHQ) and Achievement motivation test (AMT) as the measures of our study. Our findings indicated that mental health is significantly correlated with achievement motivation ( p < .001), but has no correlation with educational success ( p = .37). Also, a significant relationship was observed between achievement motivation and academic achievement ( p = .025). GHQ was not correlated with demographic factors, while academic achievement and achievement motivation are associated with the field of study and marital status respectively. Conclusively, students who are more motivated to achieve their educational and academic goals, will be more likely to be successful in their education and have stronger academic performance. Also, students with more appropriate mental health status will have higher level of motivation in their education and studies. These findings reflect the importance of maintaining the medical field students' motivation and its role in their academic success.

Keywords: academic success; anxiety; depression; mental health; motivation.

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Albeg, L.J., Castro-Olivo, S.M. The Relationship between Mental Health, Acculturative Stress, and Academic Performance in a Latino Middle School Sample. Contemp School Psychol 18 , 178–186 (2014). https://doi.org/10.1007/s40688-014-0010-1

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DOI: 10.5772/INTECHOPEN.95766
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Relation between Student Mental Health and Academic Achievement Revisited: A Meta-Analysis

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Psychology, Education

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This chapter highlights the relationships between successful school adjustment and performance, mental health and social and emotional competence. It provides a strong rationale for why schools need to deliver an integrated model of teaching and learning that promotes young people’s mental health, wellbeing and academic progress. Notwithstanding what developmental cascade research tells us, there is an association for socio-economic status and school achievement as well as between socio-economic status and mental illness. Further establishing the intertwined nature of wellbeing, mental illness and school success, other research has specifically examined the social and emotional skills that children require in order to adapt to the routine and demands of school and succeed in learning. D. E. Jones, M. Greenberg and M. Crowley found that a kindergarten measure of social and emotional skills was highly predictive of young adult outcomes across education, employment, criminal activity, substance abuse and mental health.

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Positive and negative correlates of psychological well-being and distress in college students’ mental health: a correlational study.

1. Introduction

2. materials and methods, 2.1. study design and participants, 2.2. ethical approval and consent to participate, 2.3. measures and instruments, 2.4. statistical analysis, 3.1. demographic and health characteristics, 3.2. psychological well-being, 3.3. psychological distress, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Variables	N°	Percentage	M (SD)
Age			19.60 (1.68)
18–20	428	76.4
21–24	132	23.6
Sex
Women	446	79.6
Mem	114	20.4
Relationship/dating
Yes	294	43.0
No	266	47.5
Other	53	9.4
Income level *
High	164	29.3
Medium	184	32.9
Low	212	37.8
Course **
Health	343	61.3
Other	217	38.8
Type of Education
Public	416	74.2
Private	120	20.3
Military/Police	24	3.5
Academic performance ***
Mediocre	15	2.7
Sufficient	123	22.0
Good	341	60.9
Very good	74	13.20
Great	7	1.30
Physical Exercise
No	266	47.5
Yes	294	52.5
Daily	59	20.1
2 to 3 times per week	136	46.3
Once a week	83	28.2
Sleeping hours
≥8 h	33	5.9
7 to 8	205	36.6
6 to 7	230	41.1
≤5 to 6	92	16.4

Stage	Variables	Δ R	β	Std. Error
Step 1	Demographic and Relational	0.092 ***
	Gender		−3.55 *	1.57
	Income level		−1.78 *	0.79
	Satisfactory relationship/dating		3.44 *	1.36
	Age group		−4.91 **	1.57
Step 2	Academics	0.056 ***
	Health course		−1.40	1.46
	Academic performance		3.48 ***	0.92
Step 3	Health Behaviors	0.094 ***
	Physical exercise/sport		3.15 *	1.31
	Sleeping hours		4.08 **	1.31
Step 4	Psycho-Affective	0.296 ***
	Satisfaction with social support		0.42 ***	0.06
	Academic life satisfaction		0.33 **	0.12
	Total R	0.538 ***

		Δ R	β	Std. Error
Step 1	Demographic and Relational	0.051 ***
	Gender		−6.79 *	2.70
	Income level		−2.13	1.36
	Intimate relationship/dating		0.59	2.34
	Age group		6.07 *	2.71
Step 2	Academics	0.014 ***
	Year		0.51	2.39
	Health course		−0.41	2.58
	Academic performance		3.15	1.62
Step 3	Health Behaviors	0.132 ***
	Physical exercise/sport		2.79	2.27
	Hours of sleep		6.37 **	2.29
Step 4	Psycho-Affective	0.357 ***
	Satisfaction with social support		−0.52 ***	0.11
	Psychological vulnerability		0.91 ***	0.22
	Vulnerability perception		5.27 ***	1.12
	Academic life satisfaction		−0.55 **	0.19
	NLEI—Severity Index		0.02	0.47
	Total R	0.554 ***

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

Nogueira, M.J.C.; Sequeira, C.A. Positive and Negative Correlates of Psychological Well-Being and Distress in College Students’ Mental Health: A Correlational Study. Healthcare 2024 , 12 , 1085. https://doi.org/10.3390/healthcare12111085

Nogueira MJC, Sequeira CA. Positive and Negative Correlates of Psychological Well-Being and Distress in College Students’ Mental Health: A Correlational Study. Healthcare . 2024; 12(11):1085. https://doi.org/10.3390/healthcare12111085

Nogueira, Maria José Carvalho, and Carlos Alberto Sequeira. 2024. "Positive and Negative Correlates of Psychological Well-Being and Distress in College Students’ Mental Health: A Correlational Study" Healthcare 12, no. 11: 1085. https://doi.org/10.3390/healthcare12111085

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Adolescent Mental Health, Behavior Problems, and Academic Achievement

Jane d. mcleod.

1 Indiana University, Bloomington, IN, USA

Ryotaro Uemura

2 Keio University, Tokyo, Japan

Shawna Rohrman

Associated data.

Prior research on the association of mental health and behavior problems with academic achievement is limited because it does not consider multiple problems simultaneously, take co-occurring problems into account, and control for academic aptitude. We addressed these limitations using data from the National Longitudinal Study of Adolescent Health ( N = 6,315). We estimated the associations of depression, attention problems, delinquency, and substance use with two indicators of academic achievement (high school GPA and highest degree received) with controls for academic aptitude. Attention problems, delinquency, and substance use were significantly associated with diminished achievement, but depression was not. Combinations of problems involving substance use were especially consequential. Our results demonstrate that the social consequences of mental health problems are not the inevitable result of diminished functional ability but, rather, reflect negative social responses. These results also encourage a broader perspective on mental health by demonstrating that behavior problems heighten the negative consequences of more traditional forms of distress.

Sociologists maintain a long-standing interest in the social distribution of mental health problems. Literally hundreds of studies have been published on differences in levels of psychological distress or rates of psychiatric disorder based on gender, race-ethnicity, and socioeconomic status (see McLeod 2013 for a review). Although patterns are not always consistent, disadvantaged social statuses are generally associated with high levels of distress and high rates of disorder ( Thoits 2010 ), confirming the strong mark that social organization leaves on our feelings and behaviors.

Despite the dominance of research on the mental health implications of social organization, studies of the social consequences of mental health problems contribute equally to the sociological mission. In contrast to clinicians and epidemiologists, who view social consequences as indicators of disorder severity (e.g., Kessler et al. 2005 ), sociologists consider social consequences to be evidence of stigma and social exclusion (e.g., Link et al. 1987 , 1989 ). By invoking these concepts, sociologists reject the assumption that the social consequences of mental health problems follow necessarily from functional impairments in favor of the alternative that these consequences reflect fundamentally social processes.

Academic achievement is among the most thoroughly studied social consequences of mental health problems. Most studies come from outside the sociology of mental health, especially from sociology of education, social epidemiology, and developmental psychology (e.g., Campbell and von Stauffenberg 2007 ). These studies find that youth with mental health problems perform less well in school and attain lower levels of education than other youth. The association holds throughout the early life course—in elementary school (e.g., Alexander, Entwisle, and Dauber 1993 ; Farmer and Bierman 2002 ), in middle and high school ( Fletcher 2010 ; McLeod and Kaiser 2004 ; Needham 2009 ), and into the postsecondary years ( Hunt, Eisenberg, and Kilbourne 2010 ; Kessler et al. 1995 ; Miech et al. 1999 ; Needham 2009 ). It holds for multiple indicators of mental health problems, including internalizing and externalizing problems in young children ( McLeod and Kaiser 2004 ), psychological distress and depression in preadolescents and adolescents ( Needham, Crosnoe, and Muller 2004 ), and specific disorders such as attention deficit hyperactivity disorder (ADHD) ( Galéra et al. 2009 ). It also holds for behavior problems that are closely associated with mental health, including delinquency and substance use ( Lynskey and Hall 2000 ; Maguin and Loeber 1996 ; Staff et al. 2008 ). The consistency of the association across diverse mental health and behavior problems confirms their significance for attainment.

Despite many years of relevant research, empirical evidence for the association of mental health and behavior problems with academic achievement is limited in three key ways. First, few studies consider multiple problems simultaneously ( Breslau 2010 ). Many youth experience more than one problem ( Costello et al. 2003 ), which means that studies of single problems will produce biased estimates. Second, and related, even when they do consider multiple problems, studies have not determined whether some combinations of problems have stronger associations than others. To the extent that they do, estimates from studies that fail to take combinations into account may misrepresent the social consequences of mental health problems. Finally, many studies include only limited controls for academic aptitude, introducing ambiguity into the interpretation of the results. These limitations weaken our understanding of which problems matter most and why.

We address these limitations in our analysis by asking the following:

Which mental health and behavior problems have the strongest associations with future academic achievement among adolescents, independent of academic aptitude?
Which specific combinations of problems are most consequential for achievement?

We answer these questions using data from the National Longitudinal Survey of Adolescent Health, or Add Health, a prospective, longitudinal survey of U.S. adolescents. We rely on a broad definition of mental health and behavior problems and include in our analysis four types of problems that predict academic achievement: depression, attention problems, delinquency, and substance use. These problems cover the two major dimensions of emotional and behavioral problems: internalizing problems—inward-directed forms of distress such as depression and anxiety—and externalizing problems—outward-directed forms of distress such as conduct disorder and impulsive behavior. They also cover a range of “troubled and troubling” behaviors that are of concern to education scholars ( Hobbs 1982 ). Sociologists who study the social distribution of mental health problems have argued for expanding the range of outcomes beyond depression and distress to ensure a comprehensive analysis of the consequences of social inequalities for well-being ( Aneshensel, Rutter, and Lachenbruch 1991 ; Schwartz 2002 ). We advocate an equally expansive approach to the definition of mental health in analyses of social consequences.

Do Social Consequences Differ Across Problems and Why?

The answers to our questions inform a long-standing debate in research on the social consequences of mental health problems: whether the consequences are attributable to functional impairments or to negative social responses. In mental health research, this debate is associated with labeling theory (e.g., Gove 1982 ; Scheff 1966 ). Labeling theory attributes the social consequences of mental health problems to the stigma of mental illness labels and the anticipation and experience of social rejection that follow ( Link et al. 1987 ). Critics of labeling theory minimize the role of stigma and assert that the social consequences of mental health problems are attributable to the functional impairments, or symptoms, associated with the problems ( Gove 1982 ).

Although the two sides of the debate are often presented as irreconcilable, the truth likely lies in between ( Gove 2004 ). For example, in a sample of mental patients, Perry (2011) observed that symptoms of “behavioral and emotional excess” (e.g., delusions and hallucinations) elicited greater social rejection by acquaintances and strangers than symptoms of behavioral and emotional deficit (e.g., flat affect, anhedonia). In other words, even among persons who have been formally labeled, social responses depended on the specific nature of the impairment. In interactions with strangers and acquaintances, symptoms that were more overt and more disruptive to social interactions were associated with stronger negative responses.

The labeling theory debate resonates with research on the role of noncognitive traits in educational and occupational attainment. “Noncognitive trait” are productivity-related habits and traits that influence student success in formal educational settings, including aggressiveness, disruptiveness, emotional stability, self-discipline, effort, and self-esteem (see Farkas 2003 for a review). A central question in this line of research is whether noncognitive traits predict attainment independent of academic aptitude. To the extent that they do, theorists attribute the associations to subtle interactional and institutional processes that differentially value and reward student traits. Teachers prefer students who approach their work with positive attitudes, who are organized, and who are not disruptive in the classroom ( Henricsson and Rydell 2004 ; Mullins et al. 1995 ; Murray and Murray 2004 ) and they give heavy weight to work skills and habits when evaluating student performance ( Farkas 1996 ; Rosenbaum 2001 ). Beyond the classroom, schools reward students whose behaviors contribute to maintaining social order and punish students whose behaviors are disruptive or threatening ( American Psychological Association Zero Tolerance Task Force 2008 ). In short, regardless of students’ abilities to achieve, students’ behaviors importantly determine their eventual attainments.

Although different in the specifics, labeling theory and theories of noncognitive traits share a common interest in the extent to which diminished social achievements result from functional impairments or from negative social responses. At the most basic level, we engage this issue by controlling academic aptitude—the most relevant indicator of impairment—throughout the analysis. Adolescents with high levels of depression, attention problems, and delinquency score lower on standardized achievement tests and tests of verbal and performance IQ than youth with low levels of problems (see Hinshaw 1992 and Roeser, Eccles, and Strobel 1998 for reviews). Finding that the associations of youths’ problems with academic achievement remain significant with controls for academic aptitude would strengthen our claim that the associations reflect more than functional impairments.

Our analysis of differences in the associations across types of mental health and behavior problems engages this issue at a deeper level. Following from Perry's (2011) finding that different mental illness symptoms elicit different social responses in public settings, we hypothesize that different mental health and behavior problems elicit different responses in school settings. Theories of noncognitive traits imply that behaviors that signal a lack of interest in achievement and/or that are disruptive will elicit more negative responses than anxiety, passivity, and withdrawal. Because the behaviors associated with ADHD, delinquency, and substance use indicate disengagement and are more disruptive, we hypothesize that these problems will be more strongly associated with academic achievement than depression.

The few studies that have considered multiple types of problems simultaneously support this hypothesis. Attention problems, delinquency (or conduct problems), and substance use are more strongly associated with subsequent educational attainment than is depression ( Hunt et al. 2010 ; Johnson et al. 1999 ; Miech et al. 1999 ). However, none of these studies included measures of all three types of externalizing problems so we do not know whether certain externalizing problems impede academic success more than others.

Distinguishing attention problems from other externalizing problems is especially important because their interpretation is more ambiguous. Although considered an externalizing problem by clinical and epidemiological researchers, attention problems have direct bearing on learning and could be considered an indicator of aptitude. In a comprehensive analysis of data from six longitudinal studies, Duncan and colleagues (2007) observed that attention skills affected later elementary test scores net of aptitude but that other mental and behavior problems did not. If their finding extends to older ages, it would imply that, contrary to theories of noncognitive traits, non-learning-related traits have little influence on achievement processes.

Do Some Combinations of Problems Matter More Than Others?

Our second research question extends our interest in different types of problems to ask whether there are specific combinations of problems that have especially strong associations with academic achievement. The experience of co-occurring problems is an important source of heterogeneity among youth with mental health and behavior problems. Drawing on data from the Great Smoky Mountains Study, Costello and colleagues (2003) reported that adolescents with ADHD were two to seven times more likely than other adolescents to meet criteria for a depressive disorder. ADHD also increased the risk of conduct disorder—the psychiatric analogue of delinquency—by a factor of three, and substance use disorders increased the risk of mood disorders and conduct disorder by that much or more ( Costello et al. 2003 ; see Lewinsohn, Rohde, and Seeley 1995 for similar results). Evidence for the causal relationships reflected in these patterns is mixed, although it appears that the onset of ADHD and conduct problems precedes the onset of substance use and that depression precedes the onset of substance use, at least in boys (see Kessler 2004 for a review).

Accounting for combinations of problems in studies of academic achievement is important for empirical, practical, and theoretical reasons. Empirically, studies that fail to account for combinations of problems may underestimate associations because youth with the most consequential combinations are pooled with other youth in the estimates. Practically, knowing which combinations of problems are most strongly associated with academic failures informs interventions by identifying subsets of youth with greater need for services. Theoretically, knowing which combinations of problems matter most for academic achievement informs our evaluation of the relative importance of impairment versus social responses.

Clinical research suggests that youth who have more than one problem will face additional challenges in school simply because they are more impaired. For example, depressed youth who experience other mental health or behavior problems have more depressive episodes and use services at a higher rate than depressed youth who do not experience other problems ( Rohde, Lewinsohn, and Seeley 1991 ). Global functioning also declines with increases in the number of problems youth experience ( Lewinsohn et al. 1995 ). Finding that academic achievement declines with the number of problems regardless of which problems they are would suggest that increases in impairment are responsible for the association.

In contrast, theories of noncognitive traits imply that combinations of problems that involve delinquency and substance use will have especially strong associations with academic achievement because these problems are more likely to disrupt classrooms and generate punitive responses. Teachers judge oppositional behaviors as volitional and coercive, whereas they judge the behaviors associated with ADHD as involuntary ( Lovejoy 1996 ). Although most substance use occurs off school grounds, substance use that does occur in school, particularly smoking, may also be interpreted by school personnel as evidence of a defiant attitude ( Finn 2006 ). Finding that combinations of problems involving delinquency and substance use are more strongly associated with academic achievement than combinations of problems involving depression or attention problems would add support to explanations grounded in social responses.

In sum, the current study contributes to theory and research on the social consequences of mental health problems by estimating the associations of multiple problems with academic achievement simultaneously and by considering co-occurrences. The results of our analyses speak to a central debate regarding the relative importance of functional impairment versus social responses in those associations and, more generally, to theories of the role of noncognitive traits in attainment.

DATA AND METHODS

The data for the analysis come from the National Longitudinal Study of Adolescent Health, or Add Health. The Add Health is a longitudinal survey study of the health and well-being of U.S. adolescents that follows youth from the middle and high school years through the transition to early adulthood. A stratified sample of 80 high schools and 52 middle schools was selected into the study in 1994. Seventh through 12th grade youth who attended those schools were invited to participate in an in-school survey ( N = 90,118).

Of the youth who participated in the in-school survey, a randomly selected subsample of 20,745 participated in a subsequent Wave I in-home survey; an interview also was conducted with one of their parents. With the exception of the Wave I high school seniors, all respondents to the Wave I in-home survey were invited to participate in a Wave II interview approximately one year later ( N = 14,738 completed interviews) and a third wave of data collection in 2001-2002 ( N = 15,197). In 2008-2009, a fourth wave of data was collected from the original Wave I respondents ( N = 15,701). We included in our analysis 9th through 12th graders from the Wave I in-home survey who were also interviewed at Wave IV and who had valid sampling weights ( N = 6,315).

The sociodemographic profile of the sample highlights its representativeness. (See Appendix A in the online supplement [available at http://jhsb.sagepub.com/supplemental ] for complete descriptive statistics.) Women comprised just over half the sample (54 percent), and whites were the majority racial-ethnic group (54 percent), with sizable samples of African American and Latino/Latina youth (19 percent and 17 percent, respectively) and of youth with other racial-ethnic identities (10 percent). Roughly 56 percent of youth lived with both biological parents and 25 percent with single parents at Wave I, comparable to national figures ( Rawlings and Saluter 1995 ). Among the parents, 87 percent received a high school degree—also comparable to national figures ( U.S. Census Bureau 1994 )—and 34 percent received a college degree or higher.

Academic Achievement

We used two indicators of academic achievement as dependent variables: post–Wave I high school grade point average (GPA) and highest educational degree received. Our measure of post–Wave I high school GPA came from the Adolescent Health and Academic Achievement Study, a supplemental data collection that coded information from high school transcripts. Not all high school transcripts were coded, leaving a smaller sample for analyses of this outcome ( N = 4,701). We used post–Wave I GPA rather than cumulative high school GPA because pre–Wave I GPA could be a cause, rather than a consequence, of Wave I mental health and behavior problems. Using post–Wave I GPA eliminates most of the 12th graders from the analysis of this outcome but does not affect our conclusions. 1 Analyses that used a measure of GPA for all of the high school years (and that included all 12th graders) produced comparable results.

Highest educational degree received was based on respondent reports given at the Wave IV interview. We collapsed the original 13-category variable into the following: received no degree (1), received GED or high school equivalency (2), received high school diploma (3), completed technical training (4), completed some college classes (5), received bachelor's degree (6), or received higher degree (7). Although most people have completed their educations by their late 20s, some respondents may obtain more education in the future.

On average, this is a highly educated sample. The average highest degree received was 4.72, just below “some college.” The high levels of educational attainment are not surprising given that youth were recruited from schools. Nevertheless, some of these youth struggled academically. The average post–Wave I high school GPA was 2.55.

Mental Health and Behavior Problems

Unless otherwise noted, all measures of mental health and behavior problems were based on youth self-reports from the Wave I interview. To facilitate the analysis of combinations of problems, we constructed dichotomous measures for each type of problem. The pattern of main effects was the same for continuous versions of the variables.

Our measure of depression was based on a 19-item revision of the Center for Epidemiologic Studies-Depression Scale ( Radloff 1977 ). 2 The items index physical and psychological symptoms associated with depressive disorders, such as “you didn't feel like eating, your appetite was poor” and “you felt that you could not shake off the blues, even with help from your family and friends” (coded 0 = never or rarely during the past week to 3 = most of the time or all of the time during the past week). To compute a scale score, all available items were averaged for respondents who answered half of the items or more (α = .87 in this sample). We created a dichotomized measure of depression that represented youth with scores at or above the clinical cutoff (1.15 on the averaged scale; Roberts et al. 1990 ). Based on this measure, just over 10 percent of youth had high levels of depression, consistent with past epidemiological research ( Lewinsohn, Rohde, and Seeley 1998 ).

Our measure of attention problems was based on retrospective reports of ADHD symptoms from the Wave III data collection. Respondents answered 18 questions about how often they engaged in ADHD-related behaviors when they were between 5 and 12 years of age (e.g., you fidgeted with your hands or feet or squirmed in your seat; 0 = never or rarely, 1 = sometimes, 2 = often, or 3 = very often). As with the measure of depression, we averaged reports across the items whenever the respondent had valid values on half of the items or more (α = .90 in this sample). The items in the scale were based on the SNAP-IV, an instrument designed to assess ADHD in children ( Swanson 1992 ). Similar instruments, when used as retrospective reports, have shown adequate test-retest and internal consistency reliability as well as strong correlations with independent assessments of child behavior (e.g., Wierzbicki 2005 ). We dichotomized the measure of ADHD at the 80th percentile, which corresponded to a score of 1.11 (between “sometimes” and “often”). Supplemental analyses with a variable dichotomized at the 90th percentile produced substantively similar results (see endnotes). Although our cut point does not adhere to a clinical standard, high but subclinical levels of problems are associated with significant functional and social impairment ( Angold et al. 1999 ).

Following Haynie (2001) , we measured delinquency with an additive index based on youths’ self-reports of participation in 14 delinquent activities in the past year, each coded 0 (never) or 1 (one or more times). The items ranged from “painted graffiti” to “shot or stabbed someone.” We dichotomized the index at the 80th percentile to identify youth who were engaged in the very highest levels of delinquency (zero to three vs. four or more). (The end-notes describe results for a 90th percentile measure.)

Our measure of substance use was based on youths’ responses to a comprehensive series of questions about alcohol use (getting “drunk or ‘very, very high’”) in the past 12 months and about cigarette smoking and marijuana and other illicit drug use in the past 30 days. Following Nonnemaker, McNeely, and Blum (2003) , for each type of substance, we created dummy variables that distinguished youth who regularly used any of the substances from those who did not. 3 In addition, because some studies have found that cigarette use is more strongly associated with educational attainment than other types of substance use (see Breslau 2010 for a review), we present supplemental results for a disaggregated measure of substance use. The rate of regular substance use in the sample was 23 percent, with 14 percent of youth reporting regular cigarette use, 11 percent regular alcohol use, and 8 percent regular use of other drugs.

Combinations of Problems

Based on the dummy variables for specific mental health and behavior problems, we created a set of mutually exclusive dummy variables to represent specific combinations: for example, depression alone, attention problems alone, depression and attention problems. Although functionally equivalent to constructing multiplicative interactions among the dichotomous indicators, this coding strategy avoids multicollinearity and, when coupled with post-estimation contrasts, facilitates comparisons across all the different possible combinations of problems. The rates of specific combinations of problems were low, but the rates of combined problems overall were high. Roughly 29 percent of youth experienced any of the mental health problems alone, ranging from a low of 4 percent for depression to a high of 10 percent for attention problems. Roughly 20 percent of youth experienced more than one problem, ranging from a low of 0.5 percent for depression-attention problems-delinquency to a high of 4.6 percent for depression and substance use. (See Appendix A online for details.)

Academic Aptitude

We assessed academic aptitude with Wave I standardized vocabulary test scores (range = 14-149, M = 101.39) and a variable based on parents’ reports of whether the youth had a learning disability or received special education services during the 12 months prior to the Wave I parent interview (1 = yes, 0 = no, M = 0.13). 4

Social Background

Mental health problems and low academic achievement are more common among children from lower socioeconomic groups and from single-parent households ( Bradley and Corwyn 2002 ; Pallas 2003 ). To account for potential spuriousness, we controlled the following Wave I variables in all models: gender, race, whether the youth's family received public assistance, highest level of parental education, family income, and family structure. We also controlled for grade level and the youth's age because potential educational attainment is higher for the older students in the sample.

Analytic Strategy

We used regression models tailored to the two dependent variables: ordinary least squares for high school GPA and ordinal logistic for highest degree received. We began with models that included dichotomies for mental health and behavior problems along with controls for academic aptitude and social background, and then we ran models that included the full set of dummy variables for specific combinations of problems.

Although there were few missing values for most of the analysis variables, three variables were missing for more than 10 percent of the sample: family income (23 percent missing), public assistance receipt (13 percent missing), and special education/learning disability (12 percent missing). These variables came from the parents’ interviews and were missing when parents did not participate or did not report the information. To preserve cases for the analysis, we estimated models using the ICE (Imputation by Chained Equations) and MICOMBINE multiple imputation procedures in STATA 11.1 ( Royston 2005a , 2005b ). 5 Together, these procedures generated 15 data sets—each with a different set of missing data imputations. We estimated the models within each data set and combined the results to yield a single set of parameter estimates. 6

We began by predicting high school GPA and highest degree from the indicators for specific mental health and behavior problems, with controls for social background and academic aptitude. For each outcome, we estimated models that included each mental health or behavior problem alone followed by models that included all problems together. Coefficients for the control variables are omitted for parsimony of presentation.

According to Table 1 , attention problems, delinquency, and substance use all were associated with lower high school GPA whether considered alone or simultaneously. In contrast, depression was only significantly associated with high school GPA in models that did not include the other problems. The associations of mental health and behavior problems with high school GPA were strong in terms of statistical significance and modest in magnitude. Based on Model 5 in Table 1 , youth with high levels of attention problems had GPAs that were .14 points lower on average than youth who did not, roughly 16 percent of a standard deviation; the difference between youth who did and did not have high levels of delinquency (b = –.15) was of the same magnitude 7 ; the difference based on regular substance use was a little more than twice as large (b = –.34). The final model disaggregates the substance use measure by type of substance: cigarette, alcohol, and other drugs (including marijuana). Cigarette and alcohol use were both significantly associated with lower GPA, but the difference for cigarette use was about three times as large.

Coefficients from Regressions of Academic Achievement on Specific Problems

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
= 4,701)
Depression	–.16 (.05)				–.06 (.05)	–.05 (.05)
Attention problems		–.19 (.04)			–.14 (.04)	–.14 (.04)
Delinquency			–.27 (.04)		–.15 (.04)	–.17 (.04)
Substance use				–.40 (.04)	–.34 (.04)
Cigarette use						–.34 (.05)
Alcohol use						–.11 (.05)
Drug use						–.06 (.07)
= 6,315)
Depression	–.35 (.09)				–.08 (.09)	–.07 (.09)	–.03 (.12)
Attention problems		–.40 (.09)			–.28 (.08)	–.27 (.08)	–.14 (.08)
Delinquency			–.72 (.08)		–.42 (.08)	–.47 (.08)	–.20 (.09)
Substance use				–.99 (.09)	–.84 (.09)		–.45 (.09)
Cigarette use						–.98 (.10)
Alcohol use						–.12 (.09)
Drug use						–.11 (.17)
Post-Wave 1 GPA							1.41 (.06)

Notes : Coefficients for GPA are from ordinary least squares regression models; coefficients for highest degree are from ordinal logistic regression models. Standard errors are in parentheses. All models included controls for academic aptitude and social background.

Although the coefficients for the control variables are not included in the table, many were roughly the same size as those for mental health and behavior problems. For example, the coefficient for youth living in a single parent household was –.17, for those receiving public assistance was –.16, and for black versus white race was –.15—about the same size as for attention problems and delinquency. The coefficients for other common predictors of academic achievement, including special education/learning disability status (b = –.22) and having a parent who attained a college education or more (b = .29), were smaller than those for substance use. Thus, although the associations for mental health and behavior problems were modest, they were comparable to those for other major sociodemographic predictors.

The results for highest degree received closely paralleled those for high school GPA. Depression, attention problems, delinquency, and substance use all were associated with receiving a lower degree when considered alone. The association of depression with highest degree became nonsignificant when the other problems were included in the model. Exponentiated coefficients provide estimates of the odds of receiving the next highest degree with a one-unit increase in the independent variable, in this instance, the shift from having a low to a high value on the dichotomous indicators for mental health and behavior problems. For attention problems, having a high versus low level of problems was associated with .76 (e –.28 ) times the odds (i.e., 24 percent lower odds) of receiving the next highest degree. The comparable odds for delinquency and substance use were .66 (e –.42 ) and .43 (e –.84 ), respectively. 8 When we disaggregated the substance use measure by type (Model 6), cigarette use was the only type of substance use that was significantly associated with highest degree. Youth who smoked regularly had .38 times the odds of receiving the next highest degree.

One could argue for including high school GPA as a control in the models for highest degree received because it captures student performance ability. We did not include it in our initial models, because although GPA is a function of student performance, it also is a function of student motivation, teachers’ expectations for student performance, and teachers’ evaluations of student behavior ( Farkas 1996 ; Hamre and Pianta 2001 ). To the extent that GPA reflects motivations, expectations, and evaluations, controlling for GPA controls for part of the process through which youth problems affect achievement. Nevertheless, to provide the most conservative estimates, we estimated an additional model with GPA added (Model 7 in Panel B of Table 1 ). The coefficients for delinquency and substance use were reduced by about half but remained significant, and the coefficient for attention problems became marginally significant ( p = .093). Thus, even with the most stringent controls for academic aptitude, behavior problems had significant associations with educational attainment. 9

Specific Combinations of Problems

The next set of models takes us to our second research question: the role of combinations of problems in academic achievement. Our initial models may misrepresent the associations of problems with academic achievement if some combinations of problems are more consequential than others.

The left panel of Table 2 presents coefficients for models predicting high school GPA from specific combinations of problems, with controls for academic aptitude and social background. The variables for each specific problem represent youth who experienced that problem alone; the other variables represent youth who experienced specific combinations of problems (e.g., Dp-A = depression and attention problems). We did not estimate models for combinations involving the disaggregated measure of substance use as the sample sizes were prohibitively small.

Coefficients and Predicted Values from Regression of High School GPA on Combinations of Problems

			Predicted GPA for Respondents with:
	b		Depression	Attention Problems	Delinquency	Substance Use
Dp	.01	(.07)	2.70
A	–.11	(.05)		2.60
D	–.13	(.05)			2.58
S	–.34	(.06)				2.37
Dp-A	–.29	(.14)	2.41	2.41
Dp-D	–.24	(.09)	2.46		2.46
Dp-S	–.48	(.15)	2.23			2.23
A-D	–.36	(.12)		2.35	2.35
A-S	–.53	(.11)		2.18		2.18
D-S	–.48	(.07)			2.22	2.22
Dp-A-D	–.56	(.13)	2.14	2.14	2.14
Dp-A-S	–.36	(.13)	2.34	2.34		2.34
Dp-D-S	–.47	(.12)	2.23		2.23	2.23
A-D-S	–.58	(.11)		2.13	2.13	2.13
Dp-A-D-S	–.74	(.16)	1.97	1.97	1.97	1.97

Notes : Dp = depression alone; A = attention problems alone; D = delinquency alone; S = substance use alone.

Coefficients from ordinary least squares regression. Standard errors are in parentheses. Model included controls for academic aptitude and social background. N = 4,701.

With the exception of youth who only experienced depression, youth who experienced every other problem, alone or in combination, had lower average GPAs than youth without any problems. This indicates that depression in and of itself is much less consequential for academic achievement than are behavior problems.

The right panel of Table 2 presents predicted GPAs for youth with specific combinations of problems, along with the significance of post-estimation tests that compared the coefficients for youth with only one problem to those with combinations involving that same problem. For example, the significance level for Dp-D in the first column of the right panel represents the significance of the difference in the coefficients for the Dp-D versus Dp groups. We estimated the significance of the comparisons for all groups but only present the comparisons in “nested” groups, that is, Dp versus Dp-A and Dp versus Dp-A-D but not Dp-A versus D-S.

Our results support three main conclusions. First, according to the coefficients, attention problems, delinquency, and substance use were associated with earning a lower GPA but depression was not. Even in the absence of additional problems, youth who experienced any one of the externalizing problems had diminished achievement. Second, according to the post-estimation comparisons, youth who experienced combinations of problems generally had lower GPAs than youth who experienced only one problem, although the magnitude of the difference varied. Youth with depression who experienced other problems had lower GPAs than youth who experienced depression alone: the predicted GPA for youth with depression was 2.70 whereas that for youth with depression and delinquency was 2.46 and that for youth with depression and substance use was 2.23. Youth with attention problems who also experienced delinquency and substance use had lower GPAs than youth with attention problems alone. Youth with delinquency had lower GPAs when they also experienced substance use but not when they experienced depression or attention problems. In addition, adding depression, attention problems, or delinquency did not significantly diminish the low GPAs associated with substance use. These results confirm that depression did not increase the educational risk associated with other problems and that substance use had the most consistent association with academic achievement.

Third, although not immediately obvious from this table, youth with three or more problems generally did not have significantly lower GPAs than did youth with two problems. Indeed, with one exception, post-estimation comparisons revealed no significant differences in GPA for youth with two versus three problems. (The exception was for youth with depression, attention problems, and delinquency [Dp-A-D] who had significantly lower GPAs than youth with depression and delinquency [Dp-D; p = .03]). Some of the absence of difference can be attributed to small sample sizes but some reflects a true absence of meaningful differentiation. A quick glance at the predicted GPAs across the groups indicates that although there are differences, the differences do not follow an obvious pattern with respect to the number of problems youth experienced. Some predictions in the two-problem combinations were lower than those in the three-problem combinations and vice-versa. The predicted GPA for the group with all four problems was lower than for all others but, based on post-estimation comparisons, was not significantly different on a consistent basis. 10

Comparable results for highest degree received are presented in Appendix B online. We did not include high school GPA as a control in the model based on the reasoning given earlier: GPA may mediate the associations of youth mental health and behavior problems with educational attainment. 11

The three main conclusions from the analysis of high school GPA held for highest degree received, but some of the specific results differed. First, as for GPA, delinquency (b = –0.47, p < .01) and substance use (b = –0.83, p < .001) were associated with receiving a lesser degree and depression was not (b = –0.08, p = .64). However, unlike for GPA, attention problems were not associated with highest degree received either (b = –0.21, p = .10). This suggests that attention problems alone matter less for educational attainment than they do for high school performance. Second, youth who experienced more than one problem generally achieved a lower degree than youth who experienced only one problem. As for GPA, adding depression did not significantly diminish attainment for youth with other problems and adding substance use did. However, unlike for GPA, attention problems were associated with lower educational attainment for youth with depression and substance use, and delinquency was not associated with diminished educational attainment for youth with depression or attention problems. This suggests that co-occurring attention problems heighten the risk of low attainment associated with other problems. Third, having three or more problems was not associated with significantly lower attainment than having two problems.

Our analysis addressed two key questions for research on the association of mental health and behavior problems with academic achievement: Which specific problems most strongly predict academic achievement? Are certain combinations of problems more consequential than others? We found that attention problems, delinquency, and substance use were more strongly associated with achievement than was depression and that youth who experienced two or more problems earned lower GPAs and attained lower levels of education than youth who experienced only one problem. More specifically, having an additional externalizing problem—especially substance use—was associated with a significant decline in GPA and attainment. The associations were independent of academic aptitude, lending credence to the general conclusion that mental health and behavior problems are important determinants of status attainment outcomes ( Farkas 2003 ).

Our results confirm previous evidence that regular substance use is associated with diminished academic achievement (e.g., Breslau et al. 2008 ; Ellickson et al. 1998 ; Lynskey and Hall 2000 ; Newcomb et al. 2002 ). Although many studies that evaluate the association of substance use with academic achievement do not consider multiple substances simultaneously, those that do support our finding that cigarette use is the strongest predictor ( Breslau 2010 ). Why cigarette use is so consequential has not yet been established. One obvious explanation—that the association is spurious due to social background, risk propensity, cognitive impairment, or behavioral disinhibition—has been disconfirmed ( Lynskey and Hall 2000 ; Staff et al. 2008 ). Another explanation attributes the association to diminished academic motivation, especially as reinforced by deviant peer associations ( Breslau 2010 ). However, in our analysis of highest degree received, we controlled GPA, a reasonable if imperfect proxy for motivation, and observed significant residual effects of cigarette use. We propose an alternative: that cigarette use is more likely to elicit strong negative sanctions from school authorities and that these sanctions diminish attainment. According to the National Center on Addiction and Substance Use (2011) , most schools respond to substance use punitively rather than therapeutically ( American Psychological Association Zero Tolerance Task Force 2008 ). Because youth are more likely to smoke cigarettes at school than they are to use alcohol ( Finn 2006 ), the effect of punitive disciplinary policies would be especially pronounced for that substance ( McNeely, Nonnemaker, and Blum 2002 ).

We also observed that delinquency was negatively associated with GPA and educational attainment whether considered alone or in combination with other problems. Research on delinquency and academic success typically assumes that poor academic performance predicts future delinquency ( Maguin and Loeber 1996 ). Because our measure of delinquency was taken prior to the measures of academic achievement, our analysis provides strong evidence for the reverse. Further strengthening our conclusion, the association of delinquency was independent of attention problems—a commonly discussed precursor of both delinquency and poor academic performance ( Satterfield, Hoppe, and Schell 1982 ).

Previous evidence for the association of depression with academic achievement is both more limited and more mixed. Major epidemiological surveys find that early-onset depression is not associated with subsequent educational attainment independent of other mental disorders (e.g., Breslau et al. 2008 ; Miech et al. 1999 ). In contrast, studies using the Add Health report a significant effect of adolescent depression on high school completion and college entry ( Fletcher 2008 ; Needham 2009 ). Our analyses establish that the discrepancy is attributable to our controls for other mental health and behavior problems: We observed a significant effect for depression that became nonsignificant with controls for other problems. For scholars interested in the reciprocal associations between social disadvantage and psychological distress (for which depression is a common indicator), the most important implication of our results is that the causation runs predominantly from disadvantage to distress rather than the reverse. That the same is not true for more disruptive problems highlights the need for a more differentiated framework for the associations of mental health and behavior problems with social attainments.

Such a framework could begin with the debate that motivated our analysis: whether the social consequences of mental health problems are the inevitable result of functional impairments or whether they depend on negative social responses. Three findings support the latter position. First, we observed significant associations independent of academic aptitude (and, for substance use and delinquency, independent of attention problems). Second, problems that disrupt activities, challenge teacher authority, and are likely grounds for punitive action—especially delinquency and substance use—were more strongly associated with academic achievement than depression. Third, although youth who experienced multiple problems achieved less academically than youth who experienced only one problem, academic achievement did not decline consistently with the number of problems. Together, these findings provide strong evidence that impairments associated with behavior problems are not the sole determinants of their negative social consequences. In the case of highest degree received, the learning impairments associated with attention problems do appear to increase the risk of low attainment associated with other problems, but attention problems alone are inconsequential.

Beyond its contributions to this debate, our analysis carries lessons for sociologists of mental health and stratification researchers. For sociologists of mental health, our results suggest the value of incorporating a broad array of emotional and behavioral dysfunctions into our analyses, consistent with the practice of developmental scholars ( Achenbach et al. 1981 ). Sociologists of mental health have tended to maintain a narrow focus on “distress,” often as represented by depression. Some scholars conceptualize substance use as a “masculine” expression of distress analogous to depression (e.g., Aneshensel et al. 1991 ), but others assert that substance use represents “bad behavior” rather than distress ( Mirowsky and Ross 1995 ). Delinquency and other threatening behaviors have receive comparatively little attention ( Schwartz 2002 ; Umberson, Williams, and Anderson 2002 ). Our results demonstrate that even if distinct from “distress,” substance use and delinquency are important components of the complex problems youth experience in the real world ( Mirowsky and Ross 2002 ) and that they have profound social consequences.

For stratification researchers, our analysis demonstrates that the thoughts, feelings, and behaviors that characterize mental health and behavior problems are relevant to how adolescents fare in the educational system. Most research on noncognitive traits focuses on conscientious work habits, self-confidence, and the like. Although there is direct evidence for teachers’ differential evaluations of these traits ( Farkas 2003 ), less is known about how teachers evaluate mental health and behavior problems. As the effect sizes for these problems are greater than or equal to those for traditional predictors of academic achievement, they deserve greater attention from stratification researchers.

We acknowledge three features of our analysis that limit our conclusions. First, we did not have access to formal diagnostic measures. Although our dichotomized indicators captured high levels of problems, the associations we observed might be stronger for problems that meet diagnostic criteria for major mental disorders ( Breslau 2010 ). Second, we lacked information on academic achievement in earlier grades. The process we observed may have begun much earlier in youths’ educational careers, driven either by the effects of early mental health problems on later academic achievement or the effects of early academic failures on later mental health problems ( Roeser et al. 1998 ). Consistent with the latter possibility, some research indicates that youth initiate cigarette use as a means of coping with poor achievement ( Schulenberg et al. 1994 ). Third, the causal sequence among the co-occurring problems youth experienced remains uncertain. Co-occurring substance use could represent a coping response for youth with depression or attention problems. If so, models that include substance use would underestimate the effects of other problems because they control for the mediational process that produces their effects. Arguing against this possibility, depression and attention problems were more weakly associated with academic achievement even before the control for substance use was introduced. Nevertheless, knowing more about the origins of co-occurring problems would deepen our understanding of how mental health and behavior problems come to affect achievement.

Despite these limitations, our analysis advances research on the social consequences of mental health problems in three important ways: by highlighting the special relevance of disruptive problems within school settings; by demonstrating that youth with co-occurring problems face more academic challenges than other youth; and by providing evidence that although abilities powerfully shape future attainments, so too do subtle evaluative processes.

Supplementary Material

Acknowledgments.

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by grant R01 HD050288 from the National Institute of Child Health and Human Development.

Jane D. McLeod is professor of sociology and associate dean for social & historical sciences and graduate education in the College of Arts and Sciences at Indiana University. Jane is currently engaged in research on the life course outcomes of high-risk youth and is co-editor (with Ed Lawler and Michael Schwalbe) of the Handbook of the Social Psychology of Inequality .

Ryotaro Uemura is a project assistant professor in the International Center at Keio University, Japan. His current research examines the associations of individuals’ social structural positions with social attitudes in cross-national context.

Shawna Rohrman is a doctoral candidate in sociology at Indiana University. Her dissertation research examines racial disparities in health and well-being during the transition to adulthood. She is also interested in the health effects of prejudice and discrimination among youth.

1 Of the youth who reported that they were in 12th grade at Wave 1, 23 had valid post–Wave I GPA information. Five were in their 4th year of high school at Wave I and continued into a 5th year after that; three were in the 4th year of high school at Wave I but were taking 11th grade courses in that year; one was in the 3rd year of high school at Wave I but had an average of 12th-grade-level courses in that year; and 13 were in the 3rd year of high school at Wave I and had an average of 11th-grade-level courses in that year. According to the education data, one of these youth started high school in 1999, which we take to be a coding or reporting error.

2 The items in the Add Health were not identical to the original CES-D scale. Two items from the original scale were not used: “I had crying spells” and “My sleep was restless.” One new item, “You felt life was not worth living,” was added. One item was reworded from “I could not get going” to “It was hard to get started doing things.”

3 Youth were defined as regular smokers if they smoked 20 days or more. Regular alcohol use was defined as getting “drunk or very, very high” two or three days a month or more. Regular drug use was defined as using any drugs three or more times in the past 30 days.

4 In preliminary analyses, we also included youth reports of how often they had trouble getting their homework done and a measure of whether they had ever repeated a grade as indicators of aptitude. Because these indicators could be consequences of mental health and behavior problems, we removed them from our analysis. Results were substantively the same.

5 Most variables were imputed with prediction equations that included all other variables in the analysis. The exceptions were race, parental education, and family structure, which were passively imputed. Per von Hippel (2007) , we included dependent variables in the imputation procedure but restricted the analysis of each dependent variable to cases with valid values. Because of this restriction, the sample size for the analysis of GPA was 4,701. No cases with valid values for the weight variable had missing values on highest degree received.

6 There is some debate about how many imputed data sets should be used in this type of procedure. Although the use of five data sets has become standard practice, some evidence suggests that five may not be sufficient in all cases ( Graham, Olchowski, and Gilreath 2007 ). We chose 15 data sets for our analysis based on Graham et al.'s analysis of power and efficiency in multiple imputation procedures for models with modest effect sizes.

7 Results for models with measures dichotomized at the 90th percentile show a similar overall pattern with somewhat larger coefficients (e.g., b = –.22 for attention problems and b = –.29 for delinquency in Model 5; p < .001 for both).

8 The 90th percentile dichotomizations of attention problems and delinquency showed similar patterns. The 90th percentile delinquency measure had a stronger association (b = –.68, p < .001 in Model 5). The 90th percentile attention problems measure had a slightly weaker association (b = –.25, p < .001 in Model 5).

9 Previous studies of depression and academic achievement have found a stronger association for young women than for young men (e.g., Needham 2009 ). We reestimated the models for women and men separately; we also estimated gender differences in the associations of problems using multiplicative interactions. Depression was not significantly associated with academic achievement for either group or either outcome. Moreover, there was only one significant gender difference in the associations: attention problems predicted a lower GPA and lower degree attainment more strongly for girls than for boys.

10 The average GPA for the group with all four problems was significantly lower than that for the group with depression and delinquency (Dp-D, p < .01), attention problems and delinquency (A-D, p < .05), and depression and attention problems (Dp-A, p < .05) but was not lower than that for any other group with 2 or more problems.

11 In supplemental analyses where we controlled for GPA, several of the coefficients remained statistically significant: substance use alone (S; b = –.44, p < .001) and four combinations involving substance use (A-S, b = –.61, p < .001; D-S, b = –.69, p < .001; Dp-A-S, b = –1.30, p < .001; and A-D-S, b = –.77, p < .01). The significant contrasts of nested problem combinations all involved attention problems (A vs. A-S, p < .01; A vs. C-A-S, p < .001; A vs. A-D-S, p < .01).

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Open access
Published: 29 November 2023

The relationship between social support and academic engagement among university students: the chain mediating effects of life satisfaction and academic motivation

Chunmei Chen 1 ,
Fei Bian 2 &
Yujie Zhu 3

BMC Public Health volume 23 , Article number: 2368 ( 2023 ) Cite this article

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Metrics details

University students’ academic engagement has a significant impact on their academic performance and career development.

In order to explore the influential mechanisms of social support on university students’ academic engagement and the mediating role of academic motivation and life satisfaction, this study used the Adolescent Social Support Scale, University Students’ Academic Engagement Scale Questionnaire, Adolescent Student Life Satisfaction Scale and University Students’ Academic Motivation Questionnaire, to conduct a questionnaire survey and empirical analysis on 2106 Chinese university students.

(1) social support significantly and positively predicts academic engagement; (2) social support influences academic engagement through the mediating effect of life satisfaction; (3) social support influences academic engagement through the mediating effect of academic motivation; (4) life satisfaction and academic motivation play a chain mediating role in the effect of social support on academic engagement.

Conclusions

This study contributes to understanding the underlying mechanisms of the relationship between social support and academic engagement, which in turn provides insights for universities and the departments concerned to make measures to improve the level of university students’ academic engagement.

Peer Review reports

Introduction

According to the Ministry of Education of the People’s Republic of China, as of 2022, there was a total of 3,013 higher education institutions in the country. Among them, there were 1,239 general undergraduate schools with a total of 19,656,400 students enrolled [ 1 ]. As a main force for buiding the country, university students however more and more commonly lack academic engagement in learning. This might pose a risk to the cultivation of undergraduate talents in China [ 2 ]. Considering that the level of students’ academic engagement in higher education institutions is increasingly recognized as a valid indicator of institutional excellence [ 3 ]. Thus, one key factor to higher education development is to improve university students’ academic engagement, which in turn enhances the quality of talent cultivation in undergraduate universities [ 4 ]. Academic engagement reflects the quality of students’ participation, investment, commitment into and recognition with schools and related activities to improve students’ performance [ 5 ]. It is the extent to which students are committed to schools and are motivated to learn [ 6 ]. Only when students are actively engaged in the learning process can they have meaningful and lasting learning experiences [ 7 , 8 ]. Academic engagement encompasses the behavioral (e.g., participation in academic and social activities), affective (comprised of students’ attitudes, interests, and values), and cognitive (e.g., motivational goals and the application of learning strategies, etc.) dimensions of an individual’s engagement in the learning process [ 9 ]. More specifically, academic engagement includes students attending classes, completing assignments, interacting with peers and instructors, and enrolling and participating in extracurricular activities [ 10 ]. Academic engagement also refers to the time and efforts that students invest into the activities with educational purposes [ 11 ]. Academic engamenment is typically characterized as vitality (representing energy, willingness, and persistence in the face of difficulties), dedication (understanding the meaning of the work, being enthusiastic, inspired, and proud of the work), and absorption (focusing on the work) [ 12 ]. High levels of academic engagement are necessary for students’ success in universities [ 13 , 14 ]. Scholars have confirmed that university students’ learning effectiveness depends on their academic engagement [ 15 ]. Their academic engagement is a better predictor to students’ learning outcomes such as critical thinking, cognitive activities, and reading and writing skills [ 16 ]. Research into university students’ academic engagement can help reduce dissatisfaction, avoid boredom, increase motivation to participate in school-related activities, and improve their achievement level [ 17 , 18 ]. University students’ academic engagement has become a topic of wide interest and discussion in the academic community. However, Studies in Fujian and Zhejiang provinces of of China have found that the overall level of university students’ academic engagement is on an average or even moderately low level [ 19 , 20 ]. It has been found that situational awareness, academic motivation, affective input, cognitive input, behavioral input and learning gain constitute a learning input mechanism that influences and constrains each other [ 21 ]. Based on 134,178 undergraduate students from 311 universities in China, a study survey has used the self-system model of motivational development as a theoretical framework.to examine the internal and external influencing mechanisms of university students’ academic engagement [ 22 ]. Another investigation of the factors influencing the lack of academic engagement of some university students in four higher education institutions in Jiangxi Province has found that there is a significant positive correlation between active cooperation, learning attitude, family factors, teaching management, school support and university students’ academic engagement [ 23 ]. In addition, scholars have explored the impact of instructor support strategies on university students’ academic engagement in online learning. University students’ academic engagement may be related to social support, life satisfaction, and academic motivation [ 24 ]. What factors are associated with university students’ academic engagement? How do these factors affect academic engagement? This study aims to investigate the relationship between university students’ academic engagement and these factors and the mechanism of their influence.

Relationship between social support and learning engagement

Social support refers to the social and psychological support that an individual receives or perceives in the environment, such as respect, care and help [ 25 ]. Psychological support refers mainly to the emotional and evaluative support provided [ 26 ], whereas non-psychological support refers mainly to instrumental and material support [ 27 ]. Evaluation support is the most common and frequently occurring social support among university students. Social support for classroom evaluation is positively correlated with academic engagement [ 28 ]. In addition, technology-supported learning environments promote greater classroom participation [ 29 ]. Students’ need for relevance or belonging is viewed as the extent to which students feel accepted and supported by teachers and peers [ 30 ]. This is even more important at the university level because students are often faced with the need to establish and maintain new relationships as their transition from high school to universities [ 31 ]. According to Hernandez et al. (2021), social support refers to any assistance and help provided to someone by others. Positive relationships are built around the provision of these supports. In educational settings, the groups that give social support are usually teachers, peers and parents. Through their research, they find that social support has a positive impact on university students’ academic engagement [ 32 ]. Guardian support helps students to be engaged in learning and achieve better academic success [ 33 ]. Moreover, good relationships with peers and teachers increase the likelihood that adolescents would demonstrate higher level of behavioral engagement in the classroom [ 34 ]. In particular, the social support provided by teachers that students find accessible plays a key role in the maintenance and development of students’ academic engagement [ 35 ]. Social support is positively related to academic engagement [ 36 , 37 , 38 ]. In summary, hypothesis H 1 is proposed.

There is a significant effect of social support on learning engagement.

Mediating effects of life satisfaction

Life satisfaction is an important cognitive measure of subjective well-being, which refers to an individual’s overall cognitive assessment of his or her life situation most of the time or over a certain period of time according to the criteria chosen by himself or herself. It is an important parameter of an individual’s life quality in a given society since it offers an overall perception and evaluation of life quality on a positive-to-negative continuum, and is an important parameter of the quality of life of individuals in a given society [ 39 ]. According to Restubog et al. (2010), social support is a form of social capital that is acquired through social interactions in various human groups. General social support from family, friends, and significant others can contribute to the development of an individual’s life satisfaction [ 40 ]. The relationship between different sources of social support (family, peers, and teachers) and life satisfaction among 1,133 Korean adolescents is investigated. It is found that the level of social support is one of the core factors of life satisfaction, which is positively correlated with life satisfaction [ 41 ]. It has been established that social support is a buffer against psychological distress [ 42 ], a factor influencing mental health [ 43 , 44 ], and a protective mechanism for life satisfaction [ 45 ], which is positively correlated with life satisfaction. In addition, life satisfaction is an important factor in mental and psychological well-being and is related to the individual’s perception of cheerfulness, which is significantly and positively correlated with students’ academic engagement [ 46 ]. This is in line with previous studies [ 47 , 48 ]. Students with higher life satisfaction across dimensions (environment, family, school, peers, self) are more academically engaged [ 49 ]. Students’ life satisfaction affects their academic engagement. In summary, hypothesis H 2 is proposed:

Life satisfaction mediates the relationship between social support and academic engagement.

The mediating effect of academic motivation

Academic motivation is the internal motivation that directly pushes students to learn, and it has an initiating, maintaining and orienting effect on learning. The nature and intensity of academic motivation directly affects the direction, progress and outcomes of university students’ learning [ 39 ]. Hsieh (2014) has pointed out that motivation consists of two dimensions: internal and external motivation. Internal motivations mainly refers to the students’ engagement in learning driven by challenges, curiosity and knowledge acquisition. External motivations represents external incentives such as grades, rewards and competitions with or evaluations from others [ 50 ]. There is a positive correlation between students’ academic motivation and the support they receive from their parents, teachers, and friends [ 51 ]. The sense of social support received from teacher-student and family relationships compensates for students’ daily low moods and thus positively affects their internal motivation to learn [ 52 ]. Camacho et al. (2021) have put forward that teachers’ social support perceived by students is a significant predictor of students’ motivation. The higher the teacher’s social support is, the lower students’ academic motivation declines [ 53 ]. It has been shown that students with higher level of social support have higher level of academic motivation [ 54 ]. In addition, students’ motivation has an impact on their academic engagement. Motivation is what drives students’ behavior, and engagement is explained through specific manifestations of students’ behavior or motivation [ 55 ]. Specific motivational structures can uniquely predict engagement [ 56 , 57 ]. Numerous studies have confirmed that academic motivation is positively correlated with academic engagement [ 36 , 58 , 59 , 60 ]. In summary, hypothesis H 3 is proposed.

Academic motivation mediates the relationship between social support and academic engagement.

Chain-mediated effects of life satisfaction and academic motivation

Sense of social support can help adolescents connect their goals with those of others so that they begin to gain a shared understanding of how the world works. Students with a higher sense of social support show a higher level of life satisfaction [ 61 ]. Many students’ life satisfaction reduces their life satisfaction because they are disappointed in social relationships and the constructed social support system is not strong [ 62 ]. High life satisfaction allows flexibility and autonomy for young people to pursue higher education, as well as providing them the possibility of attending various events and opportunities for personal development. Life satisfaction is a positive predictor of increased academic motivation [ 63 ]. Previous study arrives at a similar view. Students with high life satisfaction tend to believe that relying on their own abilities and behaviors can largely reduce academic stress and activate academic motivation. They always have a full enthusiasm and passion for learning [ 64 ]. Numerous studies have confirmed that life satisfaction is positively correlated with academic motivation [ 65 , 66 , 67 ]. The higher the university students’ life satisfaction is, the higher their level of academic motivation becomes. Students with high level of academic motivation do not give up easily when they encounter difficulties in their studies, instead they usually sollutions, which enables them to acquire the behavioral and cognitive strategies, information, and emotional energy demanded for task re-engagement. Therefore,. these students have high academic engagement [ 68 ]. In summary, hypothesis H 4 is proposed:

Life satisfaction and academic motivation chain mediate the effect of social support on academic engagement among university students.

This study constructed a chain mediating model to examine the effects of social support on academic engagement and the mediating role of life satisfaction and academic motivation between the two in the group of university students, with a view to providing guidance for improving university students’ academic engagement.

Research methodology

Research subjects.

Convenient sampling method was used to select subjects from several universities in China (Jimei University, Xiamen Institute of Technology, Xiamen Medical College, Guangdong University of Petrochemical Technology, etc.) to conduct a questionnaire survey, and 2,106 valid questionnaires were collected and sorted out. The age of the subjects ranged from 17 to 23 years old (M = 20.16 years old, SD = 1.31). The basic characteristics of the sample are shown in Table 1 .

Research instruments

Adolescent social support scale.

The Adolescent Social Support Scale was developed by Ye et al. in 2008 [ 39 ]. The scale includes the social support resources that the respondent receives and his/her utilization of the available resources. The Adolescent Social Support Scale is a self-report scale including three dimensions: subjective support, objective support, and support utilization, with a total of 17 entries on a five-point scale. All item scores were averaged after reverse scoring of the reverse questions, with higher mean values indicating stronger individual social support. The KMO value of the questionnaire was 0.972 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.924.

University students’ academic engagement scale questionnaire

The University Students’ Academic Engagement Scale Questionnaire was developed by Wang (2014) [ 69 ] in his doctoral dissertation. The scale contains five dimensions: active learning, teacher-student interaction, peer interaction, deep cognitive strategies and enthusiasm for learning. The first three dimensions belong to behavioral engagement, while the last two belong to cognitive and affective engagement, respectively. The questionnaire contains 22 items on a five-point scale. The scores of all items were averaged after reverse scoring the reverse questions, with higher mean values indicating higher individual academic engagement. The KMO value of the questionnaire was 0.972 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.932.

Adolescent student life satisfaction scale

The life satisfaction scale for adolescent students was developed by Zhang and He in 2004 [ 39 ]. Based on the Multidimensional Life Satisfaction Scale for Adolescents (MLSA) developed by Huebner (1994) [ 70 ], the scale was adapted to measure adolescent students’ learning and life. The life satisfaction scale for adolescent students is a self-report scale consisting of 6 dimensions of friendship, family, academics, freedom, school, and environment with 36 entries on a 5-point scale. The scores of all items were averaged after reverse scoring of the reverse questions, with higher mean values indicating higher individual life satisfaction. The KMO value of the questionnaire was 0.969 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.916.

University students’ academic motivation questionnaire

The academic motivation questionnaire for university students was developed by Tian and Pan in 2006 [ 39 ]. The questionnaire was based on Ozupal’s theoretical model of achievement motivation. The scale contains 4 dimensions of interest in knowledge, competence pursuit, reputation acquisition, and altruistic orientation, with 34 entries on a 5-point scale. The scores of all items were averaged after the reverse scoring of the reverse questions, with higher values indicating stronger individual academic motivation. The KMO value of the questionnaire was 0.976 and the Cronbach’s alpha coefficient of the questionnaire in this study was 0.93.

Research procedures

Descriptive statistics and Pearson correlation analysis were performed in this study using SPSS 26.0. In order to ensure the accuracy of the results, the variance inflation factor (VIF) method was used in the study for the covariance test (if VIF > 10, it means that there is a serious covariance problem between the variables, and the corresponding variables need to be excluded). Meanwhile, the study used model 6 in the process plug-in prepared by Hayes (2017) [ 71 ] for chained mediation effect analysis and tested the significance of the mediation effect using the bias-corrected percentile Bootstrap method. It was considered statistically significant if the 99% confidence interval did not contain a value of zero [ 72 ]. In addition, before analyzing the data, a common method bias test was performed using the Harman single-factor test [ 73 ].

Common method bias test

When the self-report method was used to collect data, the issue of common method bias may arise. Therefore, the common method bias test was performed using the Harman single-factor test. The results showed that there were eight principal components with eigenvalues greater than 1. The first principal component explained 34% of the variance, which was below the critical criterion of 40%. Therefore, there was no serious common method bias in this study.

Descriptive statistics and correlation analysis of the variables

Table 2 presented the mean and standard deviation of academic engagement, life satisfaction, social support, and academic motivation, as well as the Pearson product difference correlation coefficients between the variables. All the correlations between the variables all reached the significance level and could be further analyzed.

Relationship between social support and academic engagement: a chain mediating model

Path coefficient analysis.

The above analysis indicated a significant correlation between the variables and possible covariance. Therefore, before the effects were tested, the predictor variables in the equations were standardized and diagnosed for covariance. The results showed that the variance inflation factors for all the predictor variables (3.182, 3.277 and 1.865) were less than 5. Therefore, the data used in this study did not have serious covariance problems and were suitable for further tests of mediation effect. The process plug-in developed by Hayes was used to assess the 95% confidence interval (CI) for the mediating effect of life satisfaction and academic motivation in the effect of social support on students’ academic engagement (bootstrap sample size of 5000). The results of the chained mediation modeling were shown in Fig. 1 ; Table 3 .

The results showed that social support significantly and positively predicted academic engagement (β = 0.707, p < 0.001). With the addition of the mediating variables of life satisfaction and academic motivation, social support still significantly and positively predicted academic engagement, but with a significantly lower effect size (β = 0.061, p < 0.001). In addition, social support significantly and positively predicted life satisfaction (β = 0.643, p < 0.001) and academic motivation (β = 0.388, p < 0.001); life satisfaction significantly and positively predicted academic engagement (β = 0.757, p < 0.001); and academic motivation significantly and positively predicted academic engagement (β = 0.246, p < 0.001).

Mediation effect test

Further testing for mediating effects (see Table 4 ) found that the Bootstrap 95% CI intervals for the total indirect effects of life satisfaction and academic motivation in the effect of social support on academic engagement did not include zero. This indicated that life satisfaction and academic motivation were mediating variables in the effect of social support on academic engagement. Moreover, the total effect of social support on academic engagement was 0.707, with a direct effect of 0.061, accounting for 8.6% of the total effect, and a total indirect effect of 0.646, accounting for 91.4% of the total effect. This mediating effect was mainly composed of the following three paths:

Social Support -> Life Satisfaction -> Academic Engagement [95% CI = (0.432, 0.552), Boot SE = 0.025], with a mediating effect of 0.487, which accounted for 68.9% of the total effect, and Hypothesis 2 was supported;

Social Support -> Academic Motivation -> Academic Engagement [95% CI = (0.071, 0.130), Boot SE = 0.013], with a mediating effect of 0.095, which accounted for 13.4% of the total effect, Hypothesis 3 was supported;

Social Support -> Life Satisfaction -> Academic Motivation -> Academic Engagement [95% CI = (0.040, 0.101), Boot SE = 0.015], with a mediating effect of 0.064, which accounted for 9.1% of the total effect, and Hypothesis 4 was supported.

The effect of social support on academic engagement

The results of this study showed that social support positively predicts academic engagement, i.e., the group of university students with more social support will have higher degree of academic engagement, and conversely, the group of university students with less social support will have lower degree of academic engagement. This was consistent with the conclusions drawn from existing research. Social support can evoke positive psychological and behavioral responses by providing individuals with solutions to problems [ 74 ]. Students who receive a greater sense of social support are generally more likely to feel personally connected to the learning environment, to experience positive emotions in the classroom, and to actively use adaptive cognitive strategies for learning and participate better in learning tasks [ 37 ]. Students with more social support are better integrated into their support network and the university academic environment, thus increasing their academic achievement [ 75 ]. According to the social support theory, the provision of emotional, material, and informational support can enhance an individual’s ability and willingness to engage in specific behaviors [ 76 ]. When students receive more interactive support, they are more competent to deal with learning-related issues. Existing study shows the similar views [ 77 ]. These students believe that they can depend on their own ability to achieve their academic goals and see their studies as meaningful. For this reason, they can better persist in their studies and continue on a positive academic path [ 78 ]. Social support from parents and teachers helps develop students’ academic self-efficacy, which in turn promotes their academic engagement [ 79 ]. The social support from parents rises students’ awareness of the importance of education, promotes their desire for education and influences their academic attitudes and beliefs [ 80 ]. The social support from teachers can enhance students’ pursuit of mastery goals and interest in academic tasks, thus promoting their engagement in educational activities [ 81 ]. For example, teachers’ interaction with students during classroom activities, providing clear guidance and timely feedback to students can provide students with a sense of social support [ 32 ]. This helps deepen students’ sense of recognition with their schools and their understanding of learning value, at the same time regulating their stress and anxiety. For this reason, students’ academic engagement is positively influenced by teachers’ social support. Teachers with strong student-teacher relationships are more engaged with their students. They are able to employ strategies that engage students in deeper learning, which increases student engagement in academic activities [ 82 ]. In addition, other scholars have studied students’ online learning and reached similar conclusions. Students who receive more supportive responses and assistance for online learning invest more subjective efforts and self-regulated learning strategies in their learning activities, which increase their level of academic engagement [ 36 ].

The chain mediation model (Note: *** p < 0.001)

Mediating effect of life satisfaction

The results of this study showed that life satisfaction plays a partial mediating role between social support and academic engagement. That is, the group of university students with more social support has higher level of life satisfaction, and thus the degree of their academic engagement will be higher. This is similar to the conclusions reached by existing studies. Lin et al. (2020) note that people’s perception of social support protects them from negative outcomes and makes them less vulnerable to mental illness [ 83 ]. Additionally, individuals with more social support can more effectively cope with challenges to physical health and are more likely to overcome obstacles. For example, students who receive more support from teachers more experience joy, interest, and hope in learning and thus less anxiety, depression, or despair [ 84 , 85 ]. In the face of stress, frustration, and challenges, such students are able to actively utilize the available social resources as an alternative method to overcome emotional imbalances [ 86 ]. Social support can help individuals cope with learning demands and provides them with stronger beliefs and motivation to adapt to their learning [ 43 ]. A study of Chilean university students has found that plummeting levels of social support exacerbated the frustration of adolescent students, thereby reducing their life satisfaction. This leads to students to be prone to pessimism about learning and not to believe their ability to meet the expectations of their teachers and parents on learning, thus decreasing academic engagement [ 87 ]. More social support helps individuals better adapt to the new and changing social environments, reduce stress-induced tension, and thus enable students to experience higher level of life satisfaction [ 40 , 42 ]. Social support enhances emotional resilience in students. Close relationships with friends help adolescents better control their emotions, provide them with emotional support, and encourage self-expression and self-discovery in a stable environment, thus increasing individuals’ life satisfaction [ 43 ]. At the same time, a sense of social support can help bring about positive self-focused thinking. Social support from significant sources leads individuals to generate positive self-evaluations and develop other related personal traits, such as positive self-focus and self-concept, and subsequently leads them to have positive life experiences (e.g., life satisfaction) [ 88 ]. The higher the level of students’ life satisfaction is, the more engaged they are in learning. Social support from teachers and peers, as well as parents, can help students’ meet the basic needs for competence (i.e., a sense of effectiveness and mastery of learning), autonomy (i.e., freedom or ownership in learning), and relevance (i.e., a sense of belonging and a sense of connection to teachers and peers). Students tend to be emotionally, behaviorally, and cognitively engaged in a learning task when teachers support their independent learning [ 89 ]. When these basic needs are met, students’ life satisfaction improves and thus their engagement in academic activities is promoted [ 90 ]. A study has confirmed that more social support for adolescents would correspondingly increase their life satisfaction. They will have a more positive attitude towards their lives and be more interested in their academic and school activities, thus focusing more on their academic tasks [ 46 ]. A study of students with dyslexia has found that this type of students were exposed to fewer resources of professional tutors, leaving them with little knowledge about the options that they can have to receive substantial help. When students receive lower social support, their life satisfaction is accordingly affected. They often experience secondary emotional and motivational barriers, which in turn reduces their academic engagement [ 91 ]. In conclusion, the social support that university students receive can help them cope with the challenges from their studies, meet their basic needs and reduce negative psychological impact, thus enhancing their life satisfaction. The higher their life satisfaction is, the more they are inclined to engage in their studies.

Mediating effects of academic motivation

The results of this study showed that academic motivation plays a partial mediating role between social support and academic engagement. That is, the more the social support is, the stronger the academic motivation is, and thus the higher degree of the academic engagement becomes. This finding has been confirmed by existing studies. With today’s students facing increasing levels of anxiety, parents, teachers, and other educational professionals can help students equip with coping strategies to anxiety, thereby promoting their mental health and ultimately their academic motivation [ 92 ]. Ryan & Deci (2020), based on self-determination theory, argue that authentic, warm, and supportive environments provided by teachers and peers help students meet basic psychological needs (e.g., relatedness, etc.), which increases internal motivation to learn [ 89 ]. Students tend to have stronger academic motivation when they perceive that their teachers and peers clearly communicate expectations and values that are consistent with their own interests and provide resources and assistance, including emotional support, that are needed to fulfill those expectations and values [ 93 ]. Previous studies hold the similar view [ 94 , 95 ]. A study of English language learning among university students has found that teachers who provide social support to their students tend to create learner-centered environments and attempt to understand the emotional state of their learners, which increases students’ academic motivation. This can help students deal with challenging problems and reduce their mental stress [ 96 ]. Students who receive a higher sense of social support from their peers tend to have clearer plans and higher expectations for their own academics because of the positive motivational feedback they feel, thus promoting their own learning progress [ 97 ]. Academic engagement begins when students actively acquire knowledge based on internal motivation to learn and activate the cognitive processes required for successful problem solving [ 98 ]. Students with high levels of internal motivation are more aware of themselves and standardize their study plans, which increases the amount of effort they put into their studies [ 99 ]. Students with high degree of academic motivation may take advantage of learning opportunities in pursuit of their learning goals in order to perform better than others, rather than simply to acquire knowledge, thus increasing their degree of academic engagement [ 100 ]. This kind of students tends to become more resilient in their motivation. They are able to adjust their academic strategies in accordance with their own learning situations, take the initiative and focus on learning, and thus have a high degree of academic engagement [ 101 ]. They are able to adopt more adaptive coping strategies (particularly strategizing, help-seeking, and self-encouragement) when faced with difficult challenges, which in turn increases the degree of academic engagement [ 102 , 103 ]. Students’ motivated persistence promotes the adoption of academic strategies and provides feedback on their learning outcomes, which in turn is more conducive to subsequent academic engagement [ 104 ]. Individuals receive various types of information through social support, including the fact that they believe to be appreciated and liked by others and the fact that they believe to be valued and a part of a social network, which fully mobilizes the individual’s academic motivation and enables them to engage in learning tasks spontaneously [ 105 ]. When students are supported by their teachers, they are more likely to attend class and develop close relationships with their teachers, which increases their academic motivation. This in turn helps students to increase their self value, social self-esteem and the feeling that they can take control of their lives, which in turn promotes their level of academic engagement [ 106 ]. The more social support university students receive, the more positive feedback and incentives they receive, the stronger their level of academic motivation is. And with their increased motivation level, they are more inclined to engage in academic learning.

Chain mediation effect of life satisfaction and academic motivation

This study found that life satisfaction and academic motivation have a close relationship, and the two constitute an intermediate link in the influence path of social support -> life satisfaction -> academic motivation -> academic engagement, which has a chain mediation effect in the influence of social support on academic engagement. That is, university students with more social support have higher level of life satisfaction, which leads to stronger academic motivation and consequently higher degree of academic engagement. Social support is a buffer for university students when they are faced withf stress and adversity. Social support allows individuals to feel cared, valued, and even to have a contact in case of an emergency [ 107 ]. It helps individuals to reassess stressors as less threatening, which in turn facilitates the development of problem-solving strategies. Therefore, social support has a positive predictive effect on life satisfaction [ 108 ]. Students with high life satisfaction have the confidence to make necessary efforts to succeed, persevere in achieving their academic goals and are able to overcome the setbacks they face [ 48 , 109 ]. Students who receive social support are able to increase their life satisfaction by solving problems in a positive manner, finding appropriate ways to improve the current situation or to prevent the stressful events from recurring in the future and by encouraging themselves to regulate their emotions constructively. These coping styles allow students to return to academic activities with new energy and strategies for approaching tasks, improving the learning environment and thus greatly increasing the degree of student academic engagement [ 110 ]. Meanwhile, pre-service teachers are more likely to learn effectively in classrooms where teacher educators provide clear instructions, instrumental support and constructive feedback, support learning autonomy, and promote collaborative learning. When pre-service teachers’ needs for competence or effective learning are met, they are internally motivated to learn and adopt better knowledge integration strategies, and thus are more likely to be cognitively, emotionally, and behaviorally engaged in learning [ 58 ]. Opdenakker (2021) suggest that when students feel socially supported, they are able to gain a sense of identity in their interactions with the social environment, have more opportunities to express and expand their abilities, and thus aspire to further academic development, which fully mobilizes academic motivation that in turn increases their attention and concentration on classroom activities [ 111 ]. In addition, a number of studies have confirmed that life satisfaction is positively correlated with academic motivation. high level of life satisfaction can correspondingly increase students’ basic academic expectations and balance negative emotions such as academic irritation through, for example, rational self-regulation [ 112 ]. This kind of students are able to promote the ability to understand and regulate their own and others’ emotions and can better overcome frustrations encountered in their academic life, thus showing stronger academic motivation [ 113 ]. This is in line with existing studies. Individuals with higher life satisfaction are able to engage in more stable self-regulation and can better internalize external demands, which in turn helps individuals to acquire greater learning autonomy and better learning outcomes with stronger academic motivation [ 67 ]. Students with higher life satisfaction possess skills that make them feel empowered to achieve their goals, take control of their lives and take responsibility for their outcomes, and thus have more motivation to learn [ 114 ]. These students tend to face life with full positive emotions. This facilitates to promote meta-cognitive thinking and the use of creative learning strategies to achieve their goals [ 115 ]. They are able to enhance their academic motivation in environments characterized by a sense of safety and closeness, allowing them to persist and engage in selected tasks for longer periods of time, which in turn promotes positive learning outcomes [ 116 ]. Therefore, the mediating roles of life satisfaction and academic motivation should be given consideration when exploring the mechanisms by which social support influences academic engagement is explored.

This study examined the mechanism of how social support’s influences on university students’ academic engagement, and the chain mediating role of life satisfaction and academic motivation in the mechanism. This study found (1) social support significantly and positively predicts academic engagement; (2) social support influences academic engagement through the mediating effect of life satisfaction; (3) social support influences academic engagement through the mediating effect of academic motivation; (4) life satisfaction and academic motivation play a chain mediating role in the effect of social support on academic engagement.

These findings can help understand the inner mechanism of the relationship between social support and academic engagement, which in turn provides insights for universities and the departments concerned to improve the level of university students’ academic engagement. Our society should better understand the social support that students receive from teachers, peers, and guardians and build supportive learning environments as equal as possible for them [ 117 ]. Universities should try their best to create a comfortable accommodation environment, a safe food environment and a free and harmonious interpersonal atmosphere for students, and offer colorful extracurricular activities, so as to improve the life satisfaction of university students. At the same time, universities can help students understand the significance of learning and clarify their career development path through relevant courses and lectures, thus stimulating their academic motivation, reducing learning burnout, and enabling them to be more actively engaged in learning. On a daily basis, teachers should make efforts to improve the quantity and quality of social support provided and to facilitate interactions between students [ 118 ]. Students get more appreciation and praise from teachers and peers, which enhances their self-efficacy, stimulates their interest in learning, and makes them better complete learning tasks. Parents should also give more positive guidance to university students, provide them with appropriate financial and mental support for their studies, and help them overcome all kinds of obstacles encountered in the process of learning. Through the concerted efforts of all parties, we can jointly improve the level of university students’ academic engagement.

Contributions, limitations and prospects

Contributions.

There have been many researches on university students’ social support, which is of great significance to their healthy development. Social support is a buffer against stress [ 119 ], which can improve an individual’s psychological state [ 120 ], and increase an individual’s perception of his or her own value [ 121 ]. Social support also provides university students with a sense of security and competence [ 122 ]. Among them, there are also many studies concerned with the influence of social support on university students’ academic engagement [ 36 , 37 ]. However, there are relatively fewer separate investigations into the mediating roles of life satisfaction [ 110 ] and academic motivation [ 111 ] in this process, and extremely few ones that have simultaneously explored the chain mediating role of the two in the influence of social support on academic engagement. This study systematically and comprehensively explores the influence mechanism of social support on university students’ academic engagement. This study to a certain extent enriches the theoretical research on the influence mechanism of university students’ social support on university students’ academic engagement, which is instructive for the subsequent research. At the same time, the conclusions drawn from this study also provide references for universities and the departments concerned to improve the degree of university students’ academic engagement at the practical level, which in turn can promote the quality of talent cultivation in undergraduate universities.

Limitations and prospects

This study investigated the influence mechanism the social support on university students’ academic engagement by surveying 2,106 university students from different undergraduate universities across Chins through the principle of convenience sampling. The study has certain contributions at both the theoretical and practical levels. Of course, this study still has some limitations. First, the sample was limited by the cross-sectional data sources and remains deficient in the confirmatory nature of the causal inferences of the variables. Due to the constraints on the authors’ time and energy, only one collection of data was conducted in this study. In subsequent research, longitudinal studies can be conducted in context, with multiple collections of data tracking the development of the mechanism by which social support influences universities students’ academic engagement over time. Second, there may be selection bias and potential threats in the case of convenience sampling. Future research could adopt the method of multiple data collections. Finally, due to time constraint, the questionnaire was administered without intervention. The follow-up study could add appropriate interventions into the questionnaire process.

Data availability

The raw data supporting the conclusions of this article will be available from Chunmei Chen ([email protected]) on resonable requests.

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Acknowledgements

The authors would like to thank the participants for their involvement in this study. The authors would also like to take this opportunity to express their sincere acknowledgment to Jiancai Xu, Mingqun Que and Fangxiao Hao for their help.

2022 Guangdong Province Education Science Planning Project (Project No. 2022GXJK105); CSTVE and New - Era TVET Institute of China 2022 Annual Key Project (Project No. SZ22B05).

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CC designed the study and wrote the manuscript. FB and CC analyzed the data. FB, CC and YZ collected the data. CC, FB and YZ modified the manuscript. FB supervised the development of research and provided funding support. All authors contributed to the article and approved the submitted version.

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Chen, C., Bian, F. & Zhu, Y. The relationship between social support and academic engagement among university students: the chain mediating effects of life satisfaction and academic motivation. BMC Public Health 23 , 2368 (2023). https://doi.org/10.1186/s12889-023-17301-3

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The role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran

Maryam Latifian ORCID: orcid.org/0000-0002-7595-2087 1 ,
Mahta Alsadat Aarabi 2 ,
Sahar Esmaeili 3 ,
Kianoush Abdi ORCID: orcid.org/0000-0002-9231-5338 4 &
Ghoncheh Raheb ORCID: orcid.org/0000-0001-5392-8767 1

BMC Psychiatry volume 24 , Article number: 420 ( 2024 ) Cite this article

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The World Health Organization defines mental health as a combination of two dimensions: the negative dimension, or negative mental health, which indicates the presence of mental disorders, symptoms, and problems, and the positive dimension, or positive mental health, which includes emotions and positive personal characteristics such as self-esteem, resilience against environmental challenges, a sense of integrity, and self-efficacy. The aim of the present study was to investigate the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran, Iran.

The research method employed was a survey. 758 people participated in the study, and the samples consisted of high school students in Tehran during the academic year 2022–2023. The process of collecting information was carried out by distributing the questionnaire link through virtual networks and schools. The research utilized Young’s Internet Addiction Test, Samuels’ Academic Resilience Inventory, and Goldberg’s Mental Health Questionnaire as the research tools. Statistical tests, including Pearson’s correlation and multiple regression analysis, were employed to investigate the relationships between variables.

The effect of internet addiction on mental health (ß=0.39) is negative and significant at the 0.001 level, while the effect of academic resilience on mental health (ß=0.66) is positive and significant at the 0.001 level. These two variables collectively predict 53% of the variance in students’ mental health. This indicates that as internet addiction increases among students, their mental health significantly decreases, whereas higher levels of academic resilience correspond to higher mental health.

Conclusions

This study has elucidated the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran. Given the significance of adolescent mental health, it is imperative for healthcare professionals and other stakeholders to develop intervention and prevention models to address mental health crises and plan for the enhancement of adolescent mental health.

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Introduction

Mental health is considered one of the determinants of people’s general health. The concept encompasses feelings of well-being, self-efficacy, autonomy, resilience, a sense of belonging, self-actualization, and the realization of intellectual and emotional potential [ 1 , 2 ]. The World Health Organization defines mental health as an integration of two dimensions, negative and positive. The negative dimension, or negative mental health, indicates the presence of mental disorders, symptoms, and problems, while the positive dimension, or positive mental health, encompasses emotions and positive personal characteristics such as self-esteem, resilience against environmental challenges, a sense of integrity, and self-efficacy [ 3 ]. Treating mental illnesses and improving mental health is one of the most important goals of the World Health Organization (WHO) [ 4 , 5 ].

Adolescence is a period of life associated with the onset or exacerbation of psychological problems and disorders [ 6 , 7 ]. According to reports from the World Health Organization, approximately 10–20% of adolescents meet the necessary criteria for diagnosing mental disorders [ 8 , 9 ]. Unfortunately, in many cases, these problems persist without diagnosis [ 10 , 11 , 12 ]. If psychological problems are not diagnosed and treated promptly, they can persist chronically into later stages of life and adversely affect a person’s quality of personal and social life. Additionally, they can hinder the attainment of expected capabilities, such as personal growth and education [ 13 , 14 ]. Adolescence is one of the most critical periods in a person’s life and is associated with biological, psychological, and social changes. For this reason, adolescents may experience various crises that can lead to behavioral problems [ 15 , 16 ]. Adolescence, as a critical phase in life, confronts adolescents with extensive changes, including physical changes, reevaluation of beliefs and values, and alterations in the quality and scope of social relationships. On the other hand, these inevitable changes also increase the likelihood of engaging in risk behaviors. Therefore, the importance of paying attention to high-risk behaviors during adolescence cannot be overstated [ 17 ].

The Internet has become an essential part of our daily lives, particularly for teenagers and young adults. However, there is also a growing concern about determining the threshold for excessive Internet use and identifying when it transitions into addiction [ 18 ]. Growing up in a digital world entails risks, especially for the youngest members of society [ 19 ]. One of the common high-risk behaviors is Internet addiction [ 20 ]. Adolescence, due to its vulnerability to addiction, is a critical period during which the pattern of excessive Internet use is more likely to proliferate compared to adults [ 21 ]. With the widespread popularity of smartphones across the globe, access to the Internet has also significantly increased. The number of Internet users worldwide has reached 3.9 billion people, and the rate of Internet usage in developing countries has surged from 7.7 to 43.4% between 2005 and 2018 [ 22 ]. For instance, the prevalence of Internet addiction among high school students in Taiwan was reported to be 24.4% in 2020 [ 23 ]. Iran is not an exception to this trend, as statistics indicate a significant increase in the number of Internet users in recent years, with a 25-fold increase reported [ 24 ]. Furthermore, another study found that the overall prevalence of Internet addiction among Iranian students was 31.51% [ 25 ]. Over the past two decades, children and teenagers have exhibited a high prevalence of Internet addiction [ 26 ]. Gomez et al.‘s study on the problematic use of the Internet among Spanish teenagers reveals that a significant proportion of teenagers have experienced deviant behaviors as a result of excessive Internet and social media usage. Consequently, there is a pressing need to empower parents to moderate their children’s Internet usage [ 27 ].

Some individuals exhibit remarkable flexibility in coping with stressful factors, while others struggle, placing their mental health at risk. A review of studies has highlighted resilience as a key factor influencing individuals’ mental health. The concept of resilience gained prominence in the work of Werner in the 1970s, who defined it as the ability of individuals to maintain biological and psychological equilibrium in adverse conditions [ 28 ]. In other words, resilience refers to the positive capacities and characteristics that enable individuals to confront environmental challenges positively and shield them from stress-related mental disorders [ 29 ].

The characteristics of resilient people include flexibility, emotional intelligence, emotional insight, tenacity, hope, positive outlook, and self-confidence. These traits contribute to the development of coping skills and help protect and support individuals against various problems [ 30 ]. One of the dimensions of resilience in the educational environment is academic resilience. Students who possess academic resilience, despite facing social, cultural, and economic challenges, are able to achieve high levels of success in education [ 31 ]. Academic resilience refers to high levels of motivation for progress and performance, despite the events and stressful conditions that students face at school [ 5 ]. Bagheri et al. conducted a study to examine the influence of Internet addiction, mindfulness, and resilience on the mental health of students during the 2019 coronavirus pandemic. Their findings revealed that Internet addiction during the COVID-19 outbreak negatively impacted students’ mental health, increasing their susceptibility to depression and anxiety. The utilization of the Internet, particularly during adolescence, can significantly influence an individual’s emotional and psychological well-being [ 32 ]. This study was conducted during the COVID-19 epidemic, a period characterized by students’ reliance on virtual education and absence from physical school settings. The study aimed to examine the impact of Internet addiction, mindfulness, and resilience on the mental health of students within the context of the COVID-19 pandemic.

Mousavi et al.‘s study, titled ‘The Prevalence of Addiction to Social Networks and its Relationship with Depression, Anxiety, and Stress among Iranian Users,’ revealed a high prevalence of social network addiction among the Iranian population and its significant correlation with depression, anxiety, and stress [ 33 ]. Additionally, findings from a systematic review study conducted by Mesman et al. indicated that higher levels of resilience are associated with fewer mental health issues [ 34 ].

Based on the aforementioned studies, it is evident that the high prevalence of internet addiction worldwide, particularly among adolescents—a critical stage in life—necessitates an examination of its role in predicting their mental health. Furthermore, academic resilience emerges as a crucial issue among high school students, and its impact on predicting their mental well-being has been explored. However, given the lack of knowledge and research in the field of adolescent mental health, coupled with the paramount importance of the studied phenomenon and its dependency on sociocultural factors and living environments, there is a pressing need for attention to be directed towards this issue. This is particularly pertinent in the context of Tehran, where such studies are lacking, highlighting the urgency of addressing this gap. The examination of the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran is expected to provide essential evidence for mental health caregivers and managers involved in mental health interventions. By understanding the factors influencing mental health in this population, targeted services can be developed to improve their living conditions. Ultimately, this research aims to enhance the quality of life for these adolescents. Therefore, this research was conducted to investigate the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran, Iran.

Study design

This study was conducted using a quantitative survey method (descriptive-analytical).

Participants and sample size

The participants of this research were all students of the high school in Tehran, Iran, the year 2022–2023, of which 758 students were available by sampling method and by placing the link of the questionnaire in groups of social media networks of schools completed the questionnaires and thus participated in the study. The inclusion criteria were: (1) being a student in the high school (2), having access to the Internet and virtual space (3), living in Tehran (4), age range from 14 to 18 years (5), not having experience of substance use (6), not having a chronic medical disease (7), not having a history of psychiatric disease and (8) willingness to participate in the research. The most important exclusion criteria was that the participant lost his/her desire to cooperate in the research.

Instruments

In this research, in addition to the demographic information checklist, Young’s internet addiction test, Samuels’ academic resilience inventory and Goldberg’s mental health questionnaire were used.

Young’s internet addiction test

Kimberly Young’s Internet Addiction test was used to measure Internet addiction. This questionnaire was created by Kimberly Young in 1998 and is one of the most reliable questionnaires in the field of Internet addiction. This questionnaire is designed in the form of 20 items and scored by the Likert method. This tool measures different aspects of Internet addiction and determines whether excessive use of the Internet affects different aspects of a person’s life or not. The range of scores for this test is from 20 to 100, and a score higher than 40 indicates dependence on the Internet [ 35 ]. The scores obtained for each person were classified into three groups: (A) Normal internet users, (B) Users experiencing problems due to excessive use, and (C) Addicted users whose excessive use has led to dependency and requires treatment. Primary examinations into the validity of the IAT have shown strong internal consistency (α = 0.90–0.93) and good test-retest reliability ( r = 0.85) values [ 7 , 8 , 9 , 10 , 11 , 12 , 36 ]. In Iran, Mohagheghi et al. investigated the psychometric properties of the IAT tool, and the results of their study showed that the reliability of the questionnaire was acceptable for 20 questions, with a Cronbach’s alpha coefficient of 0.93. The face and content validity were determined using the Delphi method and by incorporating the opinions of specialists in the field of internet use [ 37 ]. In the present study, Cronbach’s alpha for this tool was found to be 0.84.

Samuels academic resilience inventory

The Samuels and Woo (2009) questionnaire was utilized to measure academic resilience [ 38 ]. This questionnaire, developed by Samuels in 2004 and later expanded upon in a study published in 2009, assesses academic resilience across three dimensions: communication skills, future orientation, and problem-oriented/ positive orientation. The original version of this questionnaire consists of 40 questions. However, after standardization for use in Iran, the number of questions was reduced to 29. The questionnaire employs a 5-point Likert scale, ranging from ‘completely disagree’ to ‘totally agree.’ A higher score on this scale indicates greater academic resilience. In his research, Samuels reported the reliability and validity of this questionnaire as 76%. Furthermore, Soltaninejad et al. standardized the questionnaire for use in Iran [ 39 , 40 ]. In Soltaninejad et al.‘s study, the psychometric properties of the Samuels Academic Resilience inventory were investigated to evaluate its suitability for Iranian students. The study involved two samples of students. The results indicated that this tool demonstrates acceptable internal consistency, with Cronbach’s alpha values for the factors ranging between 0.63 and 0.77 in the student samples [ 41 ]. In the present study, Cronbach’s alpha for this tool was found to be 0.79.

Goldberg mental health questionnaire

The Goldberg Mental Health Questionnaire, developed by Goldberg in 1979, is a self-report questionnaire consisting of 28 questions that assess four dimensions of mental health. These dimensions include physical symptoms, anxiety and insomnia, social dysfunction, and depression, with each dimension containing seven items. Respondents rate each item on a four-point Likert scale ranging from 0 to 3. Consequently, the score for each dimension falls within the range of 0 to 21, with higher scores indicating greater disorder. On average, the questionnaire takes approximately 10 to 12 minutes to complete. Goldberg and Hiller (1979) confirmed the construct and content validity of the tool and reported the reliability of the anxiety, depression, physical symptoms, and social dysfunction dimensions using the Cronbach’s alpha method as 90%, 94%, 89%, and 80%, respectively. In Iran, the validity of the questionnaire was confirmed in Kazemi et al.‘s research using exploratory and confirmatory factor analysis for construct validity. The reliability of the questionnaire was also tested in the same study, with Cronbach’s alpha reported as 0.79 [ 42 ]. In the present study, Cronbach’s alpha for this tool was found to be 0.93.”

Data implementation and data analysis

Necessary arrangements were made to obtain a license from the University of Rehabilitation Sciences and Social Health and the General Directorate of Education of Tehran Province. In Tehran, Students aged 14 to 18 study in high schools. There are about 600 high schools in this city and every school has a Telegram or WhatsApp group in which all students, teachers and school staff are members and important news and announcements are shared there. Porsline is an online questionnaire design software. Using an online questionnaire helps researchers save time and money while collecting a large amount of information in a short period. In this research, after designing the online questionnaire, the link was provided to school managers or other staff members in Tehran. They were requested to explain the research and share the questionnaire link on their respective social media groups. Students who expressed readiness to participate were given access codes to the questionnaire link, provided their parents had consented to their involvement in the research. Upon reaching an appropriate sample size, data collection was concluded. Data analysis was performed using SPSS software version 24, employing Pearson’s statistical tests and linear multiple regression to examine the relationships between variables. This study adhered to all ethical principles of research. Prior to participation, informed consent was obtained from all participants, where the purpose of the research, confidentiality measures, voluntary participation, and withdrawal procedures were explained. Arrangements were made with school officials and students’ parents, and students participated in the research with their parents’ consent. Furthermore, participants were assured that their responses would remain confidential.

The mean age of the students was 16.52 years with a standard deviation of 3.75. Of the participants, 61.08% were girls and 38.91% were boys. The demographic characteristics of the students are presented in Table 1 .

Table 2 presents the descriptive statistics of the variables internet addiction, academic resilience, and mental health, including mean, standard deviation, skewness, and kurtosis indicators. The absolute values of skewness and kurtosis for all variables are within the recommended thresholds proposed by Kleine (not exceeding 3 and 10, respectively), indicating a normal distribution of the variables. Therefore, parametric statistical tests were utilized accordingly.

In order to investigate the relationship between the variables, Pearson’s statistical test was used. Based on the information in Table 2 , the results showed that Internet addiction ( r =-0.589) has a negative and significant relationship with students’ mental health at the 0.001 level. Conversely, resilience ( r = 0.481) exhibits a significant positive relationship with students’ mental health. Additionally, the relationship between Internet addiction and academic resilience was found to be negative and significant ( r = -0.628).

To predict students’ mental health based on internet addiction and resilience, multiple regression analysis was conducted simultaneously. The internet addiction variable and components of academic resilience (Communication skills, future orientation, and problem-oriented/ positive orientation) were entered into the model. The summary of the results of the multiple regression analysis is reported in Table 3 .

In Table 3 , the linear multiple regression coefficients of internet addiction, components of academic resilience, and students’ mental health is reported as 0.764. These variables predict a total of 53% of students’ mental health changes. In this study, G*Power 3.1.9.7 software was used to calculate actual power. Effect size = 1.04, α = 0.5, ß=α0.2, and the number of predictor variables = 2 were entered into the software, and the actual power was calculated to be 0.81.

The results of the F statistic (581.86) at the 0.001 level indicate that the variables of internet addiction and components of academic resilience (Communication skills, future orientation, and problem-oriented/ positive orientation) collectively predict the mental health of students. To investigate this, linear multiple regression analysis using the simultaneous entry method was employed. Considering that the tolerance level index for all variables was less than 1 and the variance inflation factor index was less than 3, as a result, all of them can be used in regression analysis.

The effect of internet addiction on mental health (ß=0.39) is negative and significant at the 0.001 level, and the effect of components of academic resilience (Communication skills, future orientation, and problem-oriented/ positive orientation) on mental health (ß=0.653, ß=0.574, and ß=0.670) is positive and significant at the 0.001 level. Therefore, internet addiction has a negative correlation with students’ mental health. This means that with the increase in internet addiction among students, their mental health decreases significantly. Conversely, resilience demonstrates a positive correlation with students’ mental health, suggesting that higher levels of resilience correspond to better mental health outcomes.

The present study was conducted with the aim of investigating the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran. The results showed that these two variables predict a total of 51% of students’ mental health changes. There is a negative and significant relationship between the variables of Internet addiction and the mental health of students, and a positive and significant relationship between the variables of academic resilience and the mental health of students.

The findings of linear multiple regression in the study indicated that internet addiction (with the presence of components of academic resilience as covariates) has a negative relationship with the mental health of students. This means that with the increase in internet addiction among students, their mental health decreases significantly. The results of this part of the research aligned with the studies of Bagheri, Ebrahimi, Khojasteh, and Veisani, which were conducted separately from each other [ 1 , 32 , 43 , 44 ].

For instance, the results of Bagheri et al.‘s study showed that Internet addiction during the outbreak of COVID-19 damaged students’ mental health, and they were exposed to the risk of depression and anxiety [ 32 ]. Additionally, the findings of Ebrahimi and Naseri’s study indicated that social networks have a significant relationship directly with the mental health of students, and the high use of social networks will decrease their mental health [ 43 ]. The results of the study conducted by Khojesteh revealed a significant relationship between internet addiction and students’ mental health, including subscales of depression, anxiety symptoms, social functioning, sleep disorder, and physical symptoms, as well as spiritual health, including subscales of existential health and religious health in high school. Hence, it is recommended to provide regular awareness and hold specialized workshops for students, parents, and school staff to prevent mental and psychological damage from internet addiction [ 44 ]. The results of the study by Veisani et al. also confirmed that excessive use of the Internet has a significant relationship with severe depression, indicating the necessity of an educational program for students in high schools and universities regarding the correct use of the Internet [ 45 ].

On the other hand, in the present study, it was found that components of academic resilience (with the presence of internet addiction as a covariate) have a positive correlation with students’ mental health. This indicates that a higher level of resilience in students results in a higher level of mental health. The results of this part were also consistent with the research of Mesman, Ghanifar, Kehrizi, and Mortazavi, which were conducted separately. It was also determined that resilience is a vital issue in the study of psychopathology in children and adolescents, and resilience is strongly related to the mental health of children and adolescents [ 34 , 46 , 47 , 48 ].

Mesman et al., in their systematic review study, concluded that higher levels of resilience are associated with fewer psychological problems [ 34 ]. The results of Ghanifar et al.‘s study also showed that resilience is a suggestive subscale of mental health, which has the most predictive effect on social functioning and the least predictive effect on physical symptoms [ 48 ]. In the study of Kahrizi et al., it was also determined that the variables of resilience have a direct effect on emotional health and an indirect effect on the level of satisfaction with life. The first effect of improving one’s capabilities in the field of resilience is reducing mental and emotional problems, as well as increasing the level of mental health. Consequently, these improvements lead to an increase in the level of satisfaction of a person with life [ 46 ]. Also, the results of Mortazavi et al.‘s study, which was conducted by selecting nine studies and performing meta-analysis, showed that the relationship between resilience and mental health is moderate based on Cohen’s table. This research indicates that people with high levels of resilience maintain their psychological health in stressful and unfortunate situations [ 47 ].

Limitations

The limitations of the present study included a limited statistical population consisting solely of high school students in Tehran, and data collection was based on available sampling and limited to self-report and internet questionnaires. Therefore, caution should be exercised when generalizing the results to other age groups and regions.

Another limitation of the research was that not all variables identified in the literature review as predictors of students’ mental health, such as important stressors in students’ lives, were included in the study.

The results of the present study showed that the variables of internet addiction and resilience can predict the mental health of students. Therefore, it is possible to pay attention to these factors in developing educational, preventive, and therapeutic programs for this age group. To better address these issues, prevention and intervention models should be developed by healthcare personnel, experts in education, mental health, policy makers, clinical psychologists, and other stakeholders. These programs have the potential to improve the mental health of high school students, thereby promoting psychological well-being and preventing harm during adolescence, youth, and adulthood. In cases of injury, the spread of harm can be mitigated by implementing intervention programs based on these variables.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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We thank the students who participated in this study.

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Maryam Latifian & Ghoncheh Raheb

Department of Counselling, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

Mahta Alsadat Aarabi

Faculty of Social Work, University of Calgary, Calgary, Canada

Sahar Esmaeili

Department of Rehabilitation Management, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

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M.L., M.A. and S.E. Conceived and designed the analysis, collected the data, contributes data or analysis tools, performed the analysis and wrote the paper. M.L., M.A., S.E., K.A. and G.R. did edit the article. All authors reviewed the manuscript.

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Latifian, M., Aarabi, M.A., Esmaeili, S. et al. The role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran. BMC Psychiatry 24 , 420 (2024). https://doi.org/10.1186/s12888-024-05853-6

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Received : 14 November 2023

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DOI : https://doi.org/10.1186/s12888-024-05853-6

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Effects of Bullying

Bullying can affect everyone—those who are bullied, those who bully, and those who witness bullying. Bullying is linked to many negative outcomes including impacts on mental health, substance use, and suicide. It is important to talk to kids to determine whether bullying—or something else—is a concern.

Kids Who are Bullied

Kids who are bullied can experience negative physical, social, emotional, academic, and mental health issues. Kids who are bullied are more likely to experience:

Depression and anxiety, increased feelings of sadness and loneliness, changes in sleep and eating patterns, and loss of interest in activities they used to enjoy. These issues may persist into adulthood.
Health complaints
Decreased academic achievement—GPA and standardized test scores—and school participation. They are more likely to miss, skip, or drop out of school.

A very small number of bullied children might retaliate through extremely violent measures. In 12 of 15 school shooting cases in the 1990s, the shooters had a history of being bullied.

Kids Who Bully Others

Kids who bully others can also engage in violent and other risky behaviors into adulthood. Kids who bully are more likely to:

Abuse alcohol and other drugs in adolescence and as adults
Get into fights, vandalize property, and drop out of school
Engage in early sexual activity
Have criminal convictions and traffic citations as adults
Be abusive toward their romantic partners, spouses, or children as adults

Kids who witness bullying are more likely to:

Have increased use of tobacco, alcohol, or other drugs
Have increased mental health problems, including depression and anxiety
Miss or skip school

The Relationship between Bullying and Suicide

Media reports often link bullying with suicide. However, most youth who are bullied do not have thoughts of suicide or engage in suicidal behaviors.

Although kids who are bullied are at risk of suicide, bullying alone is not the cause. Many issues contribute to suicide risk, including depression, problems at home, and trauma history. Additionally, specific groups have an increased risk of suicide, including American Indian and Alaskan Native, Asian American, lesbian, gay, bisexual, and transgender youth. This risk can be increased further when these kids are not supported by parents, peers, and schools. Bullying can make an unsupportive situation worse.

The International Journal of Indian Psychȯlogy

The Relationship between Social Networking Usage and Mental Health among College Students in Bangalore: A Correlational Study

| Published: June 08, 2024

The aim of the study is to understand the relationship between social networking usage and mental health among college students and examine differences between undergraduate (UG) and postgraduate (PG). The study uses quantitative correlational analysis to find relationships between social networking use and mental health utilizing information from 65 participants. Social networking usage and mental health continuum short form (MHC-SF) standardized tools were used for assessment. Findings show a strong positive correlation, demonstrating a good relationship between increasing mental health scores and increased social networking usage. The study also reveals a weak positive correlation between UG and PG students’ use of social networking. UG students display higher use of social networking sites than their PG students. The study also identifies a slight difference in mental health between these student categories, with UG students reporting slightly better mental health. These results support earlier research that examined related relationships and offered higher education institutions and mental health professionals insightful information. This current study might have significant implications for specific interventions, methods, and support mechanisms to preserve and boost college students’ overall well-being by illuminating these intricate relationships. Therefore, an experimental research study is recommended.

Social Networking Usage , Mental Health

This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.

Received: March 27, 2024; Revision Received: June 04, 2024; Accepted: June 08, 2024

S. Sharon Sherly @ [email protected]

Article Overview

Published in Volume 12, Issue 2, April-June, 2024

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In the present research, the relationship between mental health and academic achievement in adolescents was investigated. The research adopted meta-analysis model to investigate the relationship between these two phenomena. In the meta-analysis, 13 independent studies were included, and their data were combined to display effect sizes. According to the result of the research, it was indicated ...
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The original article has been corrected.] Purpose: An inverse relationship between mental health and academic achievement is a well-known phenomenon in the scientific literature. However, how and when this association develops is not fully understood and there is a lack of longitudinal, population-based studies on young children.
Mental health and academic performance: a study on selection ...
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1 Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj, ... The aim of the present study is to assess the possible relationships between mental health, achievement motivation, and academic achievement and to study the effect of background factors on mentioned variables. ... = .37). Also, a significant relationship was ...
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In order to investigate the relationship between the variables, Pearson's statistical test was used. Based on the information in Table 2, the results showed that Internet addiction (r=-0.589) has a negative and significant relationship with students' mental health at the 0.001 level. Conversely, resilience (r = 0.481) exhibits a significant positive relationship with students' mental health.
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Kids who are bullied are more likely to experience: Depression and anxiety, increased feelings of sadness and loneliness, changes in sleep and eating patterns, and loss of interest in activities they used to enjoy. These issues may persist into adulthood. Health complaints. Decreased academic achievement—GPA and standardized test scores—and ...
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Adolescent mental health and academic performance: determining evidence-based associations and informing approaches to support in educational settings

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Relation between Student Mental Health and Academic Achievement Revisited: A Meta-Analysis

Health and Academic Achievement - New Findings

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1. Introduction

2. Methodology

2.2 Data sources

2.3 Inclusion criteria

2.4 Computation of effect sizes

2.5 Publication bias

3.1 Overall results

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5. Conclusion

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Adolescent Mental Health, Behavior Problems, and Academic Achievement

Ryotaro Uemura

Shawna Rohrman

Do Social Consequences Differ Across Problems and Why?

Do Some Combinations of Problems Matter More Than Others?

DATA AND METHODS

Academic Achievement

Mental Health and Behavior Problems

Combinations of Problems

Academic Aptitude

Social Background

Analytic Strategy

Specific Combinations of Problems

Supplementary Material

The relationship between social support and academic engagement among university students: the chain mediating effects of life satisfaction and academic motivation

Conclusions

Introduction

Relationship between social support and learning engagement

Mediating effects of life satisfaction

The mediating effect of academic motivation

Chain-mediated effects of life satisfaction and academic motivation

Research methodology

Research instruments

University students’ academic engagement scale questionnaire

Adolescent student life satisfaction scale

University students’ academic motivation questionnaire

Research procedures