The Influence of Authentic Leadership and Empowerment on New-Graduate Nurses’ Perceptions of Interprofessional Collaboration
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine new-graduate nurses' perceptions of the influence of authentic leadership and structural empowerment on the quality of interprofessional collaboration in healthcare work environments. BACKGROUND: Although the challenges associated with true interprofessional collaboration are well documented, new-graduate nurses may feel particularly challenged in becoming contributing members. Little research exists to inform nurse leaders' efforts to facilitate effective collaboration in acute care settings. METHODS: A predictive nonexperimental design was used to test a model integrating authentic leadership and workplace empowerment as resources that support interprofessional collaboration. RESULTS: Multiple regression analysis revealed that 24% of the variance in perceived interprofessional collaboration was explained by unit-leader authentic leadership and structural empowerment (R = 0.24, F = 29.55, P = .001). Authentic leadership (β = .294) and structural empowerment (β = .288) were significant independent predictors. CONCLUSIONS: Results suggest that authentic leadership and structural empowerment may promote interprofessional collaborative practice in new nurses.
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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 | 0.008 | 0.029 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| 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".