Risk Perception Mental Health Impact and Coping Strategies during Covid 19 Pandemic among Health Care Workers
Bibliographic record
Abstract
Objective: To assess the risk perception mental health impact and coping strategies during Covid-19 pandemic among health care workers. Study Design: Cross-sectional study. Place and Duration of Study: Department of Community Dentistry, Frontier Medical & Dental College, Abbottabad from 1st January 2021 to 31st December 2021. Methodology: Two hundred health care workers were given questionnaire for complete detailing their information regarding demographic, occupational, anxiety scoring and depression state. Results: The age of the health care workers was mostly within 26-40 years followed by greater than 18 years. It was observed that anxiety was presented at a mild score within doctors and other health care worker staff while it was seen to a moderate level within the nursing health care workers. Furthermore, the gender distribution of anxiety showed higher level of anxiety among females than males. Within genders a low risk perception was seen within males than females. Among the health care workers, the risk perception was highest in nurses followed by paramedic and other health care staff. Conclusion: Covid-19 has caused devastating effects on the psychological stability of the health care workers which needs to be properly assessed and addressed. Keywords: Covid-19, Health care workers, Anxiety, Risk perception
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".