The protective role of self-efficacy against workplace incivility and burnout in nursing
Bibliographic record
Abstract
BACKGROUND: Incivility has negative consequences in the workplace and remains a prevalent issue in nursing. Research has consistently linked incivility to nurse burnout and, in turn, to poor mental health and turnover intentions. To retain high-quality nurses, it is important to understand what factors might protect nurses from the negative effects of workplace mistreatment. PURPOSE: The aim of the study was to investigate the role of relational occupational coping self-efficacy in protecting nurses from workplace incivility and related burnout and turnover intentions. METHODOLOGY: A two-wave national sample of 596 Canadian nurses completed mail surveys both at Time 1 and one year later at Time 2. Structural equation modeling was used to test the hypothesized model. RESULTS: The model showed a good fit, and most of the hypothesized paths were significant. Overall, the results supported the hypothesized protective effect of relational occupational coping self-efficacy against incivility and later burnout, mental health, and turnover intentions. CONCLUSION: Relational occupational coping self-efficacy is an important protective factor against negative work behavior. PRACTICE IMPLICATIONS: Organizations should provide nurses with opportunities to build their coping strategies for managing job demands and difficult interpersonal interactions. Similarly, providing exposure to effective role models and providing meaningful verbal encouragement are other sources of efficacy information for building nurses' relational coping self-efficacy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".