Stress and psychological consequences of COVID-19 on health-care workers
Bibliographic record
Abstract
Background: The wide scope and spread of Coronavirus Disease 2019 (COVID-19) could lead to a true mental health disaster, especially in countries with high caseloads. Very few studies have assessed the impact on hospital staff. This study aimed to assess mental health changes in health-care workers (Northern Area Armed Forces Hospital-Kingdom of Saudi Arabia). Materials and Methods: This is a cross-sectional study. A survey questionnaire was designed and distributed among the participants, and the survey contained demographic questions and questions related to anxiety, worries, and fears, in addition to depressive symptoms and basic sleep profile. In addition, the psychological impacts, feelings, fears of developing COVID-19, and symptoms of posttraumatic stress disorder were assessed using the Impact of Events Scale-Revised (IES-R). Results: The mean age of the staff was 38.2 years. The examined staffs showed high levels of anxiety and depressive features: 19.3% had crying and depressed mood and 2.4% had loss of motivation; they depended mainly on social media as a source of COVID-19 information. Moreover, these features correlated positively with their Post-Traumatic features measured by the IES-R. Nearly 27.3% of the participants had their duty impacted by COVID-19 and 40.6% were affected financially. Conclusion: Our study identified a vulnerable group susceptible to psychological distress. Psychological support could also be included as counseling services and development of support systems among colleagues.
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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 | 0.001 |
| 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".