A multi-method study on the quality of the nurse work environment in acute-care hospitals: positioning Switzerland in the Magnet hospital research
Bibliographic record
Abstract
BACKGROUND: Magnet hospitals share nurse work environment characteristics associated with superior patient, nurse and financial outcomes. In Switzerland, however, it is uncertain how nurses appraise their work environments. OBJECTIVES: To describe the quality of the nurse work environment in 35 Swiss acute care hospitals and to benchmark findings based on international Magnet hospital research. METHOD: This study used two data sources: (1) the Swiss arm of the RN4CAST study; and (2) a structured literature review. Hospitals were categorised based on Magnet and non-Magnet data. Our outcome variable of interest was the quality of nurse work environment measured with the Practice Environment Scale of the Nurse Work Index (PES-NWI). RESULTS: We reviewed 13 American, Canadian, and Australian studies of acute-care hospitals. Three provided Magnet hospitals' nurse work environment data, and all included non-Magnet hospitals' data. Swiss hospitals' evaluations on nurse work environment quality varied widely, but 25% achieved scores indicating "Magnet nurse work environments". Swiss hospitals' average "Nursing manager ability" subscale scores fulfilled Magnet hospital criteria, although "Nurse participation in hospital affairs" and "Nursing staffing and resource adequacy" scores neared non-Magnet levels. CONCLUSION: On average, our results indicated high quality nurse work environments in Swiss hospitals. Implementing Magnet model organisational principles might be a valuable approach for Swiss acute-care hospitals to both improve mixed and unfavourable nurse work environments and to improve nurse and patient outcomes. National benchmarking of nurse work environments and other nurse-sensitive indicators may facilitate evaluating the impact of current developments in Swiss healthcare.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".