Mental Health Outcomes Among University Students: Identifying Risk Factors and Preventive Strategies

Mental Health and Academic Stress among University Students: A Review of Risk Factors and Intervention Strategies

University life is a specific time of life that can be very challenging and formative, as students have to deal with academic challenges while navigating life's developmental, social and financial challenges as well. The present review was conducted to compile the empirical findings pertaining to the model of linking academic stress with the mental health of University students; the sources of literature used were mainly systematic and meta-reviews published between 2015 and 2026, as well as empirical studies. Results have shown a high prevalence of depression (around 41% of the students), anxiety (34-38%), and psychological distress (up to 58%) among students, with significant variation depending on the measurement instruments, disciplines, and world regions [1,3,4]. Stress risk factors encompass academic (increased workload, pressure as shown by exams or competition), psychological (low self-efficacy, perfectionist tendencies), social (family expectations, low social support), financial and lifestyle factors, with sleep disturbances and problematic technology use being especially consistent stress risk factors [2,6,8,10]. The implications reach beyond just personal suffering, to a quantifiable drop in performance in schoolwork, increased drug and alcohol consumption and, in extreme situations, suicidal thoughts [3,9]. Intervention research shows that structured, skills-focused interventions are associated with small-to-moderate decreases in stress, anxiety and depressive symptoms, including digital interventions, which can be an effective addition to in-person interventions [13–20]. Stigma, lack of mental health literacy, and also structural constraints like long waiting times and lack of awareness of mental health services, however, continue to discourage seeking help despite its availability [21–30]. The review concludes that there is a need to move forward, and a layered stepped-care approach (universal prevention, low-intensity broadly accessible interventions and specialist care and institutional policy change) is the most promising way, although there are some methodological limitations in the field, predominance of cross-sectional design and geographical irregularity of evidence, which hamper causal understanding.

Mapping Multilevel Correlates of Adolescent Mental Health: Evidence from an Ecological Network Study.

Mental Health Challenges and Their Association With Financial Priority and Nutrition Among University Students in Bangladesh: A Cross‐Sectional Study

ABSTRACT Background and Aims University students represent a dynamic and diverse population, navigating a critical period of personal growth, academic challenges, and engaging with a wide variety of new people. However, they often face significant mental health challenges, including anxiety, depressive, and insomnia symptoms, which are exacerbated by academic pressures, financial stress, and poor dietary habits. Hence, this research sought to examine the prevalence of mental health issues among university students in Bangladesh and explore potential links between these issues and financial prioritization and nutrition. Methods This cross‐sectional survey was carried out among 450 students attending Noakhali Science and Technology University (NSTU), Bangladesh using a structured questionnaire. Anxiety, depressive, and insomnia symptoms were assessed using the Generalized Anxiety Disorder‐7 (GAD‐7), Patient Health Questionnaire‐9 (PHQ‐9), and Athens Insomnia Scale‐8 (AIS‐8), respectively. Nutritional behavior was assessed using the Health Promoting Lifestyle Profile‐II (HPLP‐II) nutrition subscale, and financial priority was evaluated using a culturally adapted version of the Castellanos and Holcomb financial prioritization instrument. Multivariable linear regression was used to examine the associations between financial priority and mental health outcomes, whereas multivariable logistic regression was used to assess the associations between nutrition and mental health outcomes. Results The prevalence of anxiety, depressive, and insomnia symptoms was 78.9%, 90.4%, and 63.6%, respectively, Anxiety, depressive, and insomnia symptoms were significantly interconnected, with positive associations between anxiety and depressive symptoms (β = 0.154, p < 0.001), anxiety and insomnia symptoms (β = 0.183, p < 0.001), and depressive and insomnia symptoms (β = 0.192, p < 0.001). Most students prioritized spending on food (84.4%) and clothing (71.3%). In multivariable linear regression analyses, prioritizing food was associated with lower anxiety (b = −2.153, p < 0.001), depressive (b = −1.743, p = 0.018), and insomnia (b = −3.519, p < 0.001) symptom scores. Conversely, prioritizing smoking was associated with increased mental health issues (p < 0.001). In multivariable logistic regression analyses, good nutrition was associated with reduced odds of anxiety (adjusted odds ratio [AOR]: 0.501, p = 0.012) and depressive (AOR: 0.425, p = 0.025) symptoms, but higher odds of insomnia symptoms (AOR: 2.03, p = 0.002). Conclusions These findings highlight the intricate relationships between financial priorities, nutrition, and psychological well‐being among Bangladeshi university students, emphasizing the need for importance of developing targeted interventions and support systems.

Structural Context and Lived Experience as Complementary Indicators of Youth Mental Health Risk.

INTRODUCTION Social and structural determinants of health (SDoH), including youths' lived experiences, shape exposure to stressors and access to resources associated with youth mental health and suicide risk. Although county-level indicators are commonly used to guide prevention efforts, it remains unclear how well they capture lived experiences of adversity or differentiate mental health risk. METHODS Data are from Project Lift Up, a national survey of adolescents and young adults aged 13-22 years (N=4,800 with residential ZIP code data) collected between 2022 and 2023. County-level SDoH indicators from the 2023 County Health Rankings were used to identify latent profiles representing distinct structural contexts. Self-reported SDoH included financial instability, food insecurity, poor home conditions, community disorder, barriers to mental health care, discrimination, and adversity. Multivariate regression models examined associations with depression/anxiety symptoms, lifetime suicidal ideation, suicide attempt, and perceived likelihood of living to age 35. Analyses were conducted in 2026. RESULTS Seven county-level SDoH profiles characterized distinct structural contexts and showed modest associations with self-reported SDoH and mental health. Self-reported SDoH were more strongly associated with outcomes than county-level context. Inclusion of self-reported SDoH improved model fit for depression/anxiety symptoms (adjusted R²: 0.16 to 0.42). Food insecurity, discrimination, adversity, and community disorder were consistently associated with higher depression/anxiety symptoms and greater odds of suicide attempt. Mental health care barriers were strongly associated with depression/anxiety and suicidal ideation but not suicide attempt. County-level profiles showed modest associations that were further attenuated after accounting for self-reported SDoH. CONCLUSIONS County-level SDoH identify geographic patterns of risk, but youths' lived experiences more strongly differentiate individual vulnerability. Prevention strategies relying solely on county-level structural indicators may miss high-risk youth, even in relatively advantaged areas. Integrating geographic targeting with screening for social adversity may improve identification of youth at risk and inform multilevel mental health prevention planning.

Influence of Demographic Factors on Mental Health among College Students

Background: Demographic factors often influence the rise in mental health issues among college students globally. Understanding these factors is essential for designing early, targeted interventions and strengthening mental health services in academic settings. Objectives: This study aimed to examine the influence of age, gender, faculty, family income, physical health problems, and psychological trauma on the mental health of college students. Methods: A cross-sectional design was used with a total of 557 Indonesian university students as participants. Data were collected using a questionnaire and the Self-Reporting Questionnaire (SRQ-20). Data were analyzed using Spearman’s Rho, Mann-Whitney, and Kruskal-Wallis tests, followed by multivariable logistic regression analysis. Results: Statistical analyses revealed significant associations between mental health status and variables including age, gender, family income, physical health problems, and psychological trauma (p < 0.05), while the faculty variable showed no significant correlation. Psychological trauma emerged as the strongest independent predictor, with students who had experienced psychological trauma being more than nine times more likely to experience mental health problems. Conclusion: Trauma-informed and demographically sensitive mental health support is crucial, especially for students who are identified as vulnerable. These insights may inform mental health nursing assessments and targeted interventions in academic settings.

Health Risk Behaviors Among University Students: A Latent Class Analysis and its Association With Mental Health.

Health behaviors (HB) are critical for university students, as they are closely associated with academic and overall well-being. This cross-sectional study aimed to explore health behaviors in a sample of university students, identify patterns of HB, and examine the association between these patterns and mental health outcomes. The sample consisted of 1085 university students enrolled at a private higher-education institution. Participants were predominantly female (83.4%), with a mean age of 22.74 years (SD = 7.03). Latent Class Analysis (LCA) was employed to identify distinct classes of HB. Three classes were identified: Class 1, labelled as the Unhealthy habits - high-stress profile (15.7%); Class 2, labelled as the Healthy/Low-risk group (68%); and Class 3, labelled as the Unhealthy habits - substance-use profile (16.3%). Significant differences in mental health outcomes were observed, with the Unhealthy habits - high stress profile displaying significantly worse mental health compared to the Healthy/Unhealthy habits - substance use profile. These findings reinforce the idea that HB do not operate in isolation but are interdependent components of a broader lifestyle pattern. The findings may inform the development of targeted interventions to promote healthier lifestyle patterns and support mental health among university students, particularly for the Unhealthy habits - high stress profile in the student population.