The Effect of Religion on Psychological Resilience in Healthcare Workers During the Coronavirus Disease 2019 Pandemic
Bibliographic record
Abstract
Background: Healthcare workers in the front line of diagnosis, treatment, and care of patients with coronavirus disease 2019 (COVID-19) are at great risk of both infection and developing mental health symptoms. This study aimed to investigate the following: (1) whether healthcare workers in general hospitals experience higher mental distress than those in psychiatric hospitals; (2) the role played by religion and alexithymic trait in influencing the mental health condition and perceived level of happiness of healthcare workers amidst the stress of the COVID-19 pandemic; and (3) factors that influence the resilience of healthcare workers at 6 weeks' follow-up. Methods: Four-hundred and fifty-eight healthcare workers were recruited from general and psychiatric hospitals, and 419 were followed-up after 6 weeks. All participants filled out the 20-item Toronto Alexithymia Scale, five-item Brief-Symptom Rating Scale, and the Chinese Oxford Happiness Questionnaire. Results: Under the stress of the COVID-19 pandemic, 12.3% of frontline healthcare workers in general hospitals reported having mental distress and perceived lower social adaptation status compared with those working in psychiatric hospitals. Christians/Catholics perceived better psychological well-being, and Buddhists/Taoists were less likely to experience mental distress. The results at 6 weeks of follow-up showed that the perceived lower social adaptation status of general hospital healthcare workers was temporary and improved with time. Christian/Catholic religion and time had independent positive effects on psychological well-being; however, the interaction of Christian/Catholic religion and time had a negative effect. Conclusions: Collectivism and individualism in the cultural context are discussed with regard to alexithymic trait and Buddhist/Taoist and Christian/Catholic religious faiths. Early identification of mental distress and interventions should be implemented to ensure a healthy and robust clinical workforce for the treatment and control of the COVID-19 pandemic.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".