Prevalence and associated factors of depression among junior healthcare professionals of public teaching hospitals of Bangladesh: An analytical cross-sectional study
Bibliographic record
Abstract
Abstract Due to the significant number and its effects on quality of life, depression is becoming a major concern worldwide. Though its prevalence among junior healthcare professionals is also increasing day by day, still very few data are available regarding this. So, we’ve conducted a study to find out the prevalence and associated factors of depression among this vulnerable population. A total of 218 participants were enrolled from two public teaching tertiary-level hospitals in Dhaka, Bangladesh from October 2018 to April 2019. Data were collected by using a self-administered questionnaire including the WHO-5 well-being index. Prevalence of major depression was found at 17.9% and poor-emotional well-being was 25.2%. Factors associated independently with major depression were those thinking to be a doctor as the wrong decision (aRRR: 6.85, 95% CI: 1.40-33.45, p=0.017) and taking sedative or anxiolytic drugs (aRRR: 4.54, 95% CI: 1.50-13.73, p=0.007). On the other hand, doing physical exercise (aRRR: 0.32, 95% CI: 0.12-0.89, p=0.028) and being satisfied in their current job position (aRRR: 0.07, 95% CI: 0.02-0.29, p<0.001) had significantly less chance of being suffering of major depression. Suicidal and self-hurting ideation was also found among 23.4% of participants. If these modifiable factors can be addressed properly and by taking necessary steps against these simply identifiable factors, unwanted incidences can be prevented especially in low- and middle-income countries. What is already known on this topic Depression is common among healthcare professionals but is still neglected especially in low- and middle-income countries. What this study adds Thinking of being a doctor as the wrong decision, taking sleeping pills, not doing physical exercise, and being not satisfied in their current job position are associated with depression among junior healthcare professionals. Suicidal and self-hurting ideation were also found high among the participants. How this study might affect research, practice or policy Early identification of major depression by simple factors may help to initiate prompt strategies that will reduce the burden of depression among junior healthcare professionals and may improve the healthcare services of low- and middle-income countries.
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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 | 0.001 | 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) | 0.002 | 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".