Magnetic work environments: Patient experience outcomes in Magnet versus non-Magnet hospitals
Bibliographic record
Abstract
BACKGROUND: The term Magnet hospital is an official designation ascribed by the American Nurses Credentialing Center for hospitals that meet specific criteria indicating they have a "magnetic work environment" for nurses. The objective of the Magnet designation is to encourage hospitals to design work in such a way as to attract and retain high-quality nurses and thus improve the quality of patient care. Empirical research has demonstrated that hospitals who earn a Magnet designation appear to have nurses who are more satisfied and committed to their work environments. Although research on whether patients are more satisfied with their care in these hospitals is still in its infancy, preliminary studies suggest that patients receiving care at Magnet-designated hospitals report more positive care experiences. PURPOSE: This study used a large secondary survey data set to explore the extent to which inpatient perceptions differed between Magnet and non-Magnet hospitals. METHODOLOGY: Ordinal logistic and multinomial logistic regression analyses were used to examine whether Magnet hospital status and positive nurse communication are related to overall hospital rating and willingness of patients to recommend the hospital. RESULTS: Results indicated that patients treated at a Magnet hospital and patients who rated nurses' communication highly were significantly more satisfied and more likely to say they would recommend the hospital. CONCLUSIONS: Evidence from this study suggests that it would be worthwhile for hospital leaders to consider organizational policies and practices consistent with the criteria put forth for Magnet hospital designation.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".