Depression, anxiety, stress, and suicidal behavior among Bangladeshi undergraduate rehabilitation students: An observational study amidst the COVID‐19 pandemic
Bibliographic record
Abstract
Background and Aims: Common mental health symptoms (CMHS) like depressive moods, anxiety, and stress are the underlying causes of suicidal behavior. The incidence of suicide is higher among Bangladeshi students. Due to the pandemic, students of health/rehabilitation sciences are at the most significant risk. This study aimed to measure the prevalence rate and predicting factors for depression, anxiety and stress, suicidal ideation, and suicide attempts in Bangladeshi undergraduate rehabilitation students. Methods: This cross-sectional study included data from 731 participants. Descriptive analyses estimated prevalence, and multivariate logistic regression models identified the factors associated with CMHS and suicidal behavior after adjusting the confounders. Results: The result shows a high prevalence of moderate to very severe CMHS and a higher risk of suicidal ideation among rehabilitation students. Sociodemographic factors, illness, behavior, institution, and subject-related issues were identified as the predicting factors of CMHS and suicidal behavior. The students suffering from mental health symptoms reported suicidal ideation and attempted at a significantly higher rate. Conclusion: To deal with CHMS and suicide risk, a holistic, supportive approach from government and academic institutions are essential for minimizing the predicting factors identified by this study. The study is helpful for the government regulatory body and policymakers to take immediate steps for preventing CMHS and suicidal behavior among rehabilitation students in Bangladesh.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".