The mediating effect of burnout on the relationship between structural empowerment and organizational citizenship behaviours
Bibliographic record
Abstract
AIM: We used Kanter's (1977) structural empowerment theory to examine the influence of structural empowerment and emotional exhaustion on healthcare professionals' use of organizational citizenship behaviours directed at the organization (OCBO) and peers (OCBI). BACKGROUND: Organizational citizenship behaviours (OCB) are discretionary behaviours that are not rewarded directly by the organization but have been linked to positive outcomes, such as increased job satisfaction and lower turnover intentions. Promoting OCB can help employees and organizations flourish despite current challenges in the healthcare system. Structural empowerment may influence the frequency and type of OCB by reducing burnout. METHOD: We conducted multiple mediated regression analyses to test two hypothesized models about relationships between empowerment, emotional exhaustion and two types of OCB (OCBI and OCBO) in a sample of 897 healthcare professionals in five Canadian hospitals. RESULTS: Emotional exhaustion was found to be a significant mediator of the relationship between empowerment and OCBO. The predicted mediation of the empowerment/OCBI relationship by emotional exhaustion was not supported. CONCLUSIONS: Exhaustion was an important mediator of empowering working conditions and OCBO, but was not significantly related to OCBI. Empowerment was significantly related to both OCBO and OCBI. IMPLICATIONS FOR NURSING MANAGEMENT: Promoting empowerment among healthcare workers may decrease burnout and promote OCB. Specific managerial strategies are discussed in the present study.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".