A Systematic Review and Meta-analysis Protocol on Depressive Symptoms Among Medical Students in South Asia Using Patient-reported Validated Assessment Tools: Prevalence and Associated Factors
Bibliographic record
Abstract
Depression among medical students in South Asia is notably higher than the global average, with prevalence rates ranging from approximately 30% to 60%. Untreated depression not only affects individual student's well-being, but also impacts academic performance and future clinical competence. This study protocol aims to synthesize evidence on the prevalence and associated factors of depressive symptoms among medical students in South Asia. The study will systematically navigate Medline (PubMed), Scopus, CINHAL, EMBASE, and APA PsycInfo for studies available before 1st May, 2025, following PRISMA guidelines for reporting and adhering to PRISMA-P standards for protocol development. The search will search for grey literature and adopt citation chain technique, using keyword truncation and string search along with standard indexing terms. Observational longitudinal studies, including cross-sectional, cohort studies, and case-control using validated patient-reported depressive symptoms measuring tools comprising South Asian medical students. Review articles, intervention studies, case reports, case series, commentaries, pre-prints, conference abstracts, protocols, unpublished research, and correspondences will not be considered. No language limitation will be applied. Two independent reviewers will screen studies, with disagreements resolved by a third reviewer. The study aims to extract information on prevalence and associated factors of depressive symptoms, conducting a narrative synthesis and meta-analysis using random effect models. Forest and funnel plots will be used to visualize findings, while heterogeneity will be assessed using the I2 statistic, with subgroup and sensitivity analysis performed to ascertain the robustness. Risk of bias (RoB) will be measured adopting the modified Newcastle-Ottawa Scale (mNOS). Statistical analysis will be conducted using R studio v.4.3.2 and GraphPad Prism v.9.0. Understanding the prevalence and risk factors is essential to guide targeted interventions and evidence-based policy reforms that support the mental well-being of future healthcare professionals. By systematically synthesizing data from observational studies, this review will provide a comprehensive synthesis of depressive symptoms, prevalence and its correlates among medical students in South Asian region, laying the groundwork for preventive strategies and improved mental health care practices.
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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 | 0.089 | 0.130 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.021 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.074 | 0.008 |
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".