Depression among medical students in Bangladesh: a systematic review and meta-analysis protocol on prevalence and associated factors
Bibliographic record
Abstract
Introduction Depression, affecting 350 million people globally, is notably prevalent among medical students, particularly in South Asia, including Bangladesh. Despite several studies, no meta-analysis has systematically examined the prevalence and contributing factors of depression to address the mental health burden. This systematic review and meta-analysis protocol aims to consolidate findings on the regional prevalence and key risk factors among Bangladeshi medical students. Methods and analysis The research team will search the Medline (Pubmed), Scopus, Web of science, Embase, PsycInfo, BanglaJOL and Google Scholar electronic databases following the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines for published studies from their inception till 1 St March 2025, using truncated and phrase-searched keywords and relevant Medical Subject Headings (MeSHs). Observational studies, including cross-sectional, cohort and case-control studies published within the timeframe and following any validated depression assessment tools, with no language restriction, reporting bangladeshi medical students, will be included for the review. Review papers, intervention studies, commentaries, preprints, meeting abstracts, protocols, unpublished studies and letters will be excluded. Two independent reviewers (SS, IA) will screen the retrieved papers using Rayyan, a web-based application, while any disagreements between them will be resolved by a third reviewer (ATS). Exposure will refer to different factors associated with depression among Bangladeshi medical students. Prevalence of depression and associated factors will be extracted. Narrative synthesis (Qualitative information) and meta-analysis (Quantitative data) will be conducted to assess the pooled prevalence using the random-effects meta-analysis (REML) model. For enhanced visualisation of the included studies, forest and funnel plots will be constructed. Heterogeneity among the studies will be assessed using the I 2 statistic, sensitivity,and subgroup analyses will be conducted, if necessary, based on study heterogeneity. The quality of the included studies will be assessed using the modified Newcastle-Ottawa Scale (mNOS) tool developed for observational study designs. All statistical analyses and visualization will be conducted using the R studio v.4.3.2 with built-in “meta”-packages and GraphPad Prism v.9.0.2. Ethics and dissemination This review will analyse existing published evidence. Findings will be submitted to a peer-reviewed journal and disseminated through conferences, policy forums and stakeholders to guide future research and interventions. PROSPERO registration number CRD 420251006480.
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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 | 0.065 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.026 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.005 |
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".