Explaining Organizational Citizenship Behavior Among Chinese Nurses Combating COVID-19
Bibliographic record
Abstract
BACKGROUND: Little is known about the associated factors with organizational citizenship behavior among Chinese nurses combating COVID-19. The aim of the present study was to investigate the relationships between autonomy, optimism, role conflict, work engagement, and organizational citizenship behavior based on moderated mediation models among Chinese nurses combating COVID-19. METHODS: This cross-sectional study was performed on a sample of 368 nurses supporting the COVID-19 epidemic in Wuhan Leishenshan Hospital, China. According to the Job Demands-Resources model, two moderated mediation models were tested, in which autonomy/optimism was associated with organizational citizenship behavior through work engagement, when role conflict served as a moderator. RESULTS: This current study found the mediating effect of work engagement and the moderating effect of role conflict on the relationship between autonomy/optimism and organizational citizenship behavior among nurses. Of note, nurses working in the COVID-19 epidemic viewed role conflict as challenge job demands rather than hindrance job demands. CONCLUSION: Based on the findings, organizational citizenship behavior can be affected by work engagement and role conflict. Nursing management is suggested to put emphasis on work engagement and role conflict among nurses supporting the COVID-19 epidemic.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".