Fear of COVID-19 and work-quality of life among nurses: The mediating role of psychological well-being
Bibliographic record
Abstract
This study was conducted in order to analyze the effect of the nurses’ quality of work life based on fear about COVID-19 and examine the psychological well-being as a moderating variable in this relationship. The survey questionnaire was administered among nurses between 1 November 2020 and 14 November 2020. The self-report survey comprised the nurse information survey, Fear of COVID-19 Scale, work quality of life scale, and psychological well-being scale as data collection tools. Data were obtained from 339 nurses. The findings show that fear of COVID-19 negatively affects nurses’ quality of work life. It has been determined that PWB plays a moderating role in this relationship. While the fear of COVID-19 negatively affects the quality of work life in nurses with low psychological well-being, there is not any kind of significant effect on the quality of work life in nurses with high psychological well-being. This result shows that as the psychological well-being of nurses’ increases, fear of COVID-19 effect on quality of work life decreases. The results of the study show that responses designed to enhance psychological well-being can enhance nurses' working conditions that could reduce the negative effects of the fear of COVID-19. There is an urgent need for clinical and policy strategies to help increase nurses’ PWB in order to increase the quality of work life by reducing fear and also anxiety among nurses fighting on the front line during COVID-19.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".