NURSES’PARTICIPATION IN PERSONAL KNOWLEDGE TRANSFER: THE ROLE OF LEADER-MEMBER EXCHANGE AND STRUCTURAL EMPOWERMENT
Bibliographic record
Abstract
Despite the current emphasis on evidence-based practice in healthcare, current findings suggest that the implementation of research findings in everyday clinical practice is haphazard and unpredictable at best with mixed outcomes. The purpose of this study was to test Kanter’s theory by examining the relationships among structural empowerment, leader-member exchange (LMX) quality, and nurses’ participation in personal knowledge transfer activities. This study was a secondary analysis of data collected in a non-experimental, predictive mailed survey design of registered nurses in Ontario (Laschinger, 2008). A random sample of 400 registered nurses who worked in urban tertiary care hospitals in Ontario yielded a final sample 234 for a 58.5% response rate. Hierarchical multiple linear regression analysis revealed that the combination of LMX and structural empowerment accounted for 9.1% of the variance in personal knowledge transfer but only total empowerment was a significant independent predictor of knowledge transfer (/?=.291, /=4.012,/?<.001). Consistent with Kanter’s Theory (1977), higher levels of empowerment and leader-member exchange quality resulted in an increase in nurses’ participation in personal knowledge transfer in practice. The results reinforce the importance and pivotal role nurse leaders have in supporting work environments that are conducive to transfer of knowledge in practice to provide evidence- based care and uphold a high standard of practice.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".