The influence of empowerment, authentic leadership, and professional practice environments on nurses’ perceived interprofessional collaboration
Bibliographic record
Abstract
AIM: The aim of this study was to examine the influence of structural empowerment, authentic leadership and professional nursing practice environments on experienced nurses' perceptions of interprofessional collaboration. BACKGROUND: Enhanced interprofessional collaboration (IPC) is seen as one means of transforming the health-care system and addressing concerns about shortages of health-care workers. Organizational supports and resources are suggested as key to promoting IPC. METHODS: A predictive non-experimental design was used to test the effects of structural empowerment, authentic leadership and professional nursing practice environments on perceived interprofessional collaboration. A random sample of experienced registered nurses (n = 220) in Ontario, Canada completed a mailed questionnaire. Hierarchical multiple regression analysis was used. RESULTS: Higher perceived structural empowerment, authentic leadership, and professional practice environments explained 45% of the variance in perceived IPC (Adj. R² = 0.452, F = 59.40, P < 0.001). CONCLUSIONS: Results suggest that structural empowerment, authentic leadership and a professional nursing practice environment may enhance IPC. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse leaders who ensure access to resources such as knowledge of IPC, embody authenticity and build trust among nurses, and support the presence of a professional nursing practice environment can contribute to enhanced IPC.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".