THE EFFECT OF NURSING LEADERSHIP AND STRUCTURAL EMPOWERMENT ON STAFF NURSE CLINICAL LEADERSHIP
Bibliographic record
Abstract
In an effort to improve patient safety, healthcare organizations have begun to focus on nursing leadership at the clinical level. The purpose of this study was to develop and test a model of staff nurse clinical leadership, defined as a process of leadership which operates at the point of care and is demonstrated in the leader behaviours of staff nurses providing direct patient care. A new measure of clinical leadership was developed for this study. Staff nurse perceptions of the influence of nurse manager leadership practices and empowering work environments on their ability to use clinical leader behaviours in their practice were examined. This was a non-experimental design study using a survey method to access registered nurses in direct care roles in acute care hospitals in Ontario. Results indicated that staff nurses have integrated clinical leadership into their practice and show staff nurse perceptions of nurse manager leadership practices were directly and positively related to empowered work environments. It was through these environments that nurse manager leadership practices influenced staff nurse clinical leadership. This study provided an important contribution to understanding the concept of staff nurse clinical leadership and how this relates to work empowerment and nurse manger leadership practices. It has important implications for nursing practice, nursing administration and nursing education.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".