Nurses' participation in personal knowledge transfer: the role of leader-member exchange (LMX) and structural empowerment
Bibliographic record
Abstract
AIM: The purpose of this study was to test Kanter's theory by examining relationships among structural empowerment, leader-member exchange (LMX) quality and nurses' participation in personal knowledge transfer activities. BACKGROUND: Despite the current emphasis on evidence-based practice in health care, research suggests that implementation of research findings in everyday clinical practice is unsystematic at best with mixed outcomes. METHODS: This study was a secondary analysis of data collected using a non-experimental, predictive mailed survey design. A random sample of 400 registered nurses who worked in urban tertiary care hospitals in Ontario yielded a final sample of 234 for a 58.5% response rate. RESULTS: 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 (β=0.291, t=4.012, P<0.001). CONCLUSIONS: Consistent with Kanter's Theory, higher levels of empowerment and leader-member exchange quality resulted in increased participation in personal knowledge transfer in practice. IMPLICATIONS FOR NURSING MANAGEMENT: The results reinforce the pivotal role of nurse managers in supporting empowering work environments that are conducive to transfer of knowledge in practice to provide evidence-based care.
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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 | 0.009 | 0.028 |
| 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 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".