Decisional involvement of senior nurse leaders in Canadian acute care hospitals
Bibliographic record
Abstract
AIM: The aim of the present study was to describe the scope and degree of involvement of senior nurse leaders (SNLs) in executive level decisions in acute care organizations across Canada. BACKGROUND: Significant changes in SNL roles including expansion of decision-making responsibilities have occurred but little is known about the patterns of SNL decision-making. METHODS: Data were collected by mailed survey from 63 SNLs and 49 chief executive officers (CEOs) in 66 healthcare organizations in 10 Canadian provinces. Regression analyses were used to examine whether timing, breadth of content expertise and the number of decision activities predicted SNL decision-making influence and quality of decisions. RESULTS: Breadth of content expertise and number of decision activities with which the SNL was involved were significant predictors of decision influence explaining 22% of the variance in influence. Overall, CEOs rated SNL involvement in decision-making higher than the SNL. CONCLUSIONS: Senior nurse leaders contribute to organizational processes in healthcare organizations that are important for nurses and patients, through their participation in decision-making at the senior team level. IMPLICATIONS FOR NURSING MANAGEMENT: Findings may be useful to current and future SNLs learning to shape the nature and content of information shared with CEOs particularly in the area of professional practice issues.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".