Prevalence of Depression, Anxiety, Stress and Suicide Ideation Among Undergraduate Medical Students in India: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Studies reported significant levels of psychological morbidity from across the globe among undergraduate medical students. Present meta-analysis aimed to provide a most up to date comprehensive insight into the prevalence of depression, stress, anxiety and suicidal ideation among undergraduate medical students in India. Material and Methods: A systematic search was conducted in three databases PubMed, Scopus and Google Scholar from July 2023 to August 2023. Quality of included studies (43 studies, N=15557) was assessed using modified Newcastle-Ottawa scale and data was analyzed using MetaXL version 5.3. Pooled estimates with 95% confidence intervals were determined using the random-effects model. Results: The pooled prevalence of depression, anxiety, stress and suicide ideation was 48% (95% CI: 41-55%) (P 0.000, I2 = 98%), 54% (95% CI 42-58%) (P =0.00, I2 = 98%), 50% (95% CI 45-63%) (P =0.001, I2 = 99%) and 21% (95% CI: 9-35%) (P =0.000, I2 = 98%) respectively. Subgroup analysis showed more females than males students were affected from depression, anxiety, stress and suicide ideation. Conclusion: High prevalence of psychological disorders in medical students in India emphasize the need for the counselling services to control this morbidity and implement long term policies and programs at institutional level.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".