Stress, Anxiety, and Depression as Psychological Distress Among College and Undergraduate Students: A Scoping Review of Reviews Guided by the Socio-Ecological Model
Bibliographic record
Abstract
Background/Objectives: College and undergraduate students around the world struggle with stress, anxiety, and depression, which have a significant negative influence on their academic performance, social interactions, and general well-being. Creating successful preventative and intervention plans requires an understanding of the many, multi-level factors that contribute to psychological discomfort. The objective of this scoping review was to use the Socio-Ecological Model (SEM) to map the determinants of psychological distress among college students in a comprehensive manner. Methods: A total of 15 review publications published between 2015 and 2024, including narrative reviews, systematic reviews, meta-analyses, and umbrella reviews, were analyzed under the guidance of PRISMA ScR. These studies synthesized evidence across various countries, including China, Iran, India, Canada, Egypt, Nigeria, Saudi Arabia, and the United States. Results: Academic pressure, financial stress, poor sleep, unhealthy coping mechanisms, and pre-existing mental health issues were all individual-level concerns, with female and minority students being more vulnerable. Strong familial ties and friendships served as protective interpersonal support. Heavy academic workloads, strict grading guidelines, a lack of mental health resources, and unwelcoming campus environments were among the institutional factors. Stigma and socioeconomic disparities are examples of community-level variables that make mental health issues worse. Conclusions: Student mental health is shaped by interrelated factors across all SEM levels. Integrated, multi-level strategies are essential to fostering supportive campuses, strengthening community networks, and implementing inclusive policies that promote mental health equity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".