Effect of Shared Governance on Nurse-Sensitive Indicator and Satisfaction Outcomes: An International Comparison
Bibliographic record
Abstract
OBJECTIVE: Researchers examined associations between Index for Professional Nursing Governance (IPNG) scores and outcomes, by US and international hospitals. BACKGROUND: Nursing governance and effects on nurse-related outcomes are not well studied. METHODS: Associations were evaluated using average IPNG scores from 2170 RNs and nurse-sensitive indicators (NSIs) and patient and RN satisfaction outcomes (n = 205 study units, 20 hospitals, 4 countries). RESULTS: International units had better IPNG shared governance scores (113.5; US = 100.6; P < 0.001), and outcomes outperforming unit benchmarks (6 of 15, 40.0%; US = 2 of 15, 13.3%). Shared governance significantly outperformed traditional governance for 5 of 20 (25.0%) US outcomes (patient satisfaction = 1, RN satisfaction = 4) and for 3 of 11 (27.3%) international (patient satisfaction = 1, RN satisfaction = 2). Internationally, self-governance significantly outperformed traditional governance and shared governance for 5 of 12 (41.7%) outcomes (NSI = 2, patient satisfaction = 3). CONCLUSIONS: Shared governance is a strategy that can be considered by nurse leaders for improving select outcomes.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".