Hospital-Acquired Conditions Reduction Program, Racial and Ethnic Diversity, and Magnet Designation in the United States
Bibliographic record
Abstract
OBJECTIVE: A key quality indicator in any health system is its ability to reduce morbidity and mortality. In recent years, healthcare organizations in the United States have been held to stricter measures of accountability to provide safe, quality care. This study aimed to explore the contextual factors driving racial disparities in hospital-acquired conditions incident rates among Medicare recipients in Magnet and non-Magnet hospitals. METHODS: A cross-sectional observational study was performed using data from Hospital-Acquired Condition Reduction Program. Performance from 1823 hospitals were used to examine the association between Magnet recognition and community's racial and ethnic differences in hospital performance on the Hospital-Acquired Condition Reduction Program. The unit of analysis was the hospital level. A propensity score matching approach was used to take into account differences in baseline characteristics when comparing Magnet and non-Magnet hospitals. The outcome measures were risk-standardized hospital performance on the Hospital-Acquired Condition Reduction Program domains and overall performance. RESULTS: Study findings show that Magnet hospitals had decreased methicillin-resistant Staphylococcus aureus (MRSA) rate (β = -0.22; 95% confidence interval, -0.36 to -0.08) compared with non-Magnet hospitals. No other statistical difference was identified. CONCLUSIONS: Results from this study show community's racial and ethnic differences in hospital-acquired conditions occurrence differ between Magnet and non-Magnet hospitals for MRSA, indicating its association with nursing practice. However, because this improvement is limited to only MRSA, there are likely opportunities for Magnet hospitals to continue process improvements focused on additional Hospital-Acquired Condition Reduction Program measures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".