Reducing burnout among nurses: The role of high-involvement work practices and colleague support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The impact of human resource practices on nurses' well-being, the underlying mechanisms involved, and the contextual factors that enhance or impede their success are not fully clear. PURPOSE: The aim of this article was to examine a moderated mediation model whereby high-involvement work practices are purported to reduce nurses' burnout via psychological empowerment, and colleague support is expected to moderate the mediating role of psychological empowerment in the high-involvement work practices-burnout link. METHODOLOGY/APPROACH: Structural equation modeling was employed on cross-sectional survey data collected from a large sample of nurses in Canada (N = 2,174). RESULTS: The findings revealed that psychological empowerment partially mediated the association between high-involvement work practices and burnout, whereas colleague support was directly associated with lower burnout rather than exerting a moderating effect. CONCLUSION: The study identifies the universality of high-involvement work practices in alleviating nurses' burnout and highlights the important role of psychological empowerment as an explanatory variable. In addition, colleague support is an important yet independent predictor of nurses' burnout. PRACTICAL IMPLICATIONS: This study identifies a strategy that can be adopted by hospital managers to help protect against nurse burnout while offering insights into the underlying process involved.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it