Prevalence of mental health symptoms and its effect on insomnia among healthcare workers who attended hospitals during COVID-19 pandemic: A survey in Dhaka city
Bibliographic record
Abstract
Abstract Background: During the COVID-19 pandemic, the high workload, risk of infection, and safety issues for family members may pose a threat to the mental health of healthcare workers (HCWs) working in hospital settings. The study aimed to find out the prevalence of anxiety, depression, and insomnia symptoms were among HCWs, as well as the factors related to these mental health issues.Methods: We conducted an online survey of HCWs employed in Dhaka city from June 6 to July 6, 2020. Symptoms of anxiety, depression, and insomnia were measured using the Generalized Anxiety Disorder, the depression module of the Patient Health Questionnaire, and the Insomnia Severity Index, respectively. The related factors of anxiety, depression, and insomnia symptoms were identified using three regression models.Results: This research included responses from 294 HCWs (mean± standard deviation age: 28.86±5.5 years; 43.5% were female). Anxiety, depression, and insomnia symptoms were found in 20.7%, 26.5%, and 44.2% of HCWs, respectively. The variable financial difficulties was commonly found as an associated factor for anxiety, depression, and insomnia symptoms. Female HCWs were more prone to mental health symptoms and insomnia compared to male HCWs (Adjusted odds ratio- AOR=2.20, 95% CI=1.27 – 3.79). The depression symptoms among HCWs were found to be a factor for insomnia (AOR=6.321, 95% CI= 3.158 – 12.650).Conclusion: In the current pandemic, the high prevalence of mental health symptoms among HCWs indicates that this occupational group being associated with increased mental distress. Increasing financial support for HCWs and providing support to female workers in care facilities could help to alleviate the burden of mental illness. Supportive, training, and educational strategies, particularly through knowledge and communication platforms, could be recommended to the care facilities, which can reduce the burden of mental health symptoms among HCWs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".