A Survey on Depressive Symptoms and Its Correlates Amongst Physicians in Bangladesh During the COVID-19 Pandemic
Bibliographic record
Abstract
Aim: The aim of this study was to determine the presence of depressive symptoms and understand the potential factors associated with these symptoms among physicians in Bangladesh during the COVID-19 pandemic. Methods: A cross-sectional study using an online survey was conducted in between April 21 and May 10, 2020, among physicians living in Bangladesh. Participants completed a series of demographic questions, COVID-19-related questions, and the Patient Health Questionnaire-9 (PHQ-9). Descriptive statistics (frequency, percentage, mean and standard deviation), test statistics (chi-squared test and logistic regression) were performed to explore the association between physicians' experience of depression symptoms and other study variables. Stepwise binary logistic regression was followed while conducting the multivariable analysis. Result: A total of 390 physicians completed the survey. Of them, 283 (72.6%) were found to be experiencing depressive symptoms. Predictors which were significantly associated with depressive symptoms were gender (with females more likely to experience depression than males), the presence of sleep disturbance, being highly exposed to media coverage about the pandemic, and fear around (a) COVID-19 infection, (b) being assaulted/humiliated by regulatory forces and (c) by the general public, while traveling to and from the hospital and treating patients during the countrywide lockdown. Conclusion: The findings of this study demonstrate that there is a high prevalence of depressive symptom among physicians especially among female physicians in Bangladesh during the COVID-19 pandemic. Immediate, adequate and effective interventions addressing gender specific needs are required amid this ongoing crisis and beyond.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".