The relationship between autonomy, optimism, work engagement and organisational citizenship behaviour among nurses fighting COVID-19 in Wuhan: a serial multiple mediation
Bibliographic record
Abstract
OBJECTIVES: High levels of organisational citizenship behaviour can enable nurses to cooperate with coworkers effectively to provide a high quality of nursing care during the outbreak of COVID-19. However, the association between autonomy, optimism, work engagement and organisational citizenship behaviour remains largely unexplored. This study aimed to test if the effect of autonomy on organisational citizenship behaviour through the mediating effects of optimism and work engagement. STUDY DESIGN: This was a cross-sectional study. SETTING: The study was conducted in the Wuhan Jinyintan Hospital in China. PARTICIPANTS: In total, 242 nurses who came from multiple areas of China to work at the Wuhan Jinyintan hospital during the COVID-19 epidemic participated in this study. METHODS: in SPSS was adopted to test the hypotheses, and a 95% CI for the indirect effects was constructed by using Bootstrapping. RESULTS: The autonomy-organisational citizenship behaviour relationship was mediated by optimism and work engagement, respectively. In addition, optimism and work engagement mediated this relationship serially. CONCLUSION: The findings of this study may have implications for improving organisational citizenship behaviour. The effects of optimism and work engagement suggest a potential mechanism of action for the autonomy-organisational citizenship behaviour linkage. A multifaceted intervention targeting organisational citizenship behaviour through optimism and work engagement may help improve the quality of nursing care among nurses supporting patients with 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".