Unveiling the burden: Depression and its determinants among Bangladeshi medical students - insights from the MINI diagnostic tool
Bibliographic record
Abstract
Abstract Mental health challenges are widespread among medical students worldwide, exacerbated by a lack of help-seeking behavior within this population. In resource-constrained healthcare systems like Bangladesh, medical students face additional barriers to expressing their struggles or seeking support. This study is the first in Bangladesh to use the MINI, a confirmatory clinical diagnostic tool, to assess the prevalence of depression among medical students. This cross-sectional study employed a convenient sampling technique, with 529 students from across Bangladesh participating via online surveys. The survey included questions on relevant sociodemographic factors and the PHQ-9 depression screening tool. Subsequently, depression was confirmed through clinical diagnosis using the MINI, conducted via online interviews over Zoom and phone calls. The study was carried out from June 2020 to February 2021, and data analysis was performed using SPSS software (version 22.0). Among the medical students surveyed using PHQ-9, 31.5% exhibited moderate levels of depression, 38.7% showed mild depression, and 29.8% reported minimal depression. Clinical diagnoses conducted using the MINI tool confirmed depression in 50 PHQ-9-positive cases. Of these, 17 (34%) were categorized as having “no depression,” 27 (54%) were diagnosed with recent depression, 16 (32%) had a history of past depression, and 15 (30%) experienced recurrent depression. Depression was more prevalent among female medical students, particularly those living away from their families from the start of their degree. First-year students were found to have the strongest association with depression. This study reported that one-third of Bangladeshi medical students experienced moderate to severe depression. The findings underscore the need for targeted psychosocial interventions and further exploration of socio-demographic factors. These results aim to guide researchers and policymakers in addressing the mental health needs of this population through effective support systems and surveillance frameworks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".