Factors Affecting the Psychological Well-Being of Health Care Workers During the COVID-19 Crisis
Bibliographic record
Abstract
Background: Health care workers (HCWs) are a group that especially suffered during the COVID-19 pandemic. In addition to facing the stress of dealing with patients and social isolation, they had to worry about being infected themselves and transmitting the infection to their families. This study evaluated the fear, anxiety, and depression experienced by HCWs during the COVID-19 crisis. Subjects and Methods: The sample size was 541 HCWs. Data collection was done using an online validated questionnaire through Google Docs, sent to HCWs by email and WhatsApp groups. We assessed depression and anxiety with the 4-item Patient Health Questionnaire-4 (PHQ-4), while evaluating fear with the Fear of COVID-19 Scale (FCV-19S). Results: A statistically significant difference was found in the perception of fear between married and unmarried people, and between those with colleagues who had died from COVID-19 infection and those without. There was a significant relation between HCWs' anxiety and a history of death from COVID-19 infection, either of friends or of close relatives. The prevalence of depression was 18.48% in the tested sample of HCWs. Participants who had close relatives or friends infected with COVID-19 showed a significantly higher degree of depression. The age group <30 and those working 20 to 30 hours weekly showed higher degrees of anxiety and depression. Conclusion: Sociodemographic variables such as age, marital status, and working area had a significant impact on the mental and psychological health of HCWs during the COVID-19 crisis. HCWs who lost patients due to COVID-19 had a significantly higher prevalence of fear, depression, and anxiety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".