The role of community‐level characteristics in comparing United States hospital performance by magnet designation: A propensity score matched study
Bibliographic record
Abstract
AIMS: To assess the impact of community-level characteristics on the role of magnet designation in relation to hospital value-based purchasing quality scores, as health disparities associated with geographical location could confound hospitals' ability to meet outcome metrics. DESIGN: This cross-sectional study was carried out between October 2021 and March 2022 using data from 2016 to 2021. METHODS: Propensity score analysis was used to match hospital and community-level characteristics, implementing nearest neighbour matching to adjust for pre-treatment differences between magnet and non-magnet hospitals to account for multi-level differences. Secondary data were obtained from all operational acute-care facilities in the United States that participated in the Centers for Medicare and Medicaid Services' hospital value-based purchasing (HVBP) program. Dependent variables were the four value-based purchasing domains that comprise the Total Performance Score (TPS; Clinical Care, Person and Community Engagement, Safety, and Efficiency and Cost Reduction). RESULTS: Magnet hospitals had increased odds for better scores in the HVBP domains of Clinical Care and Person and Community Engagement, and decreased odds for having better Safety. However, no statistically significant difference was found for the Efficiency domain or the TPS. CONCLUSION: Measuring performance equitably across organizations of various sizes serving diverse communities remains a key factor in ensuring distributive justice. Analysing the TPS components can identify complex influences of community-level characteristics not evident at the composite level. More research is needed where community and nurse-level factors may indirectly affect patient safety. IMPACT: This study's findings on the role of community contexts can inform policymakers designing value-based care programs and healthcare management administrators deliberating on magnet certification investments across diverse community settings. NO PATIENT OR PUBLIC CONTRIBUTION: For this study of US hospitals' organizational performance, we did not engage members of the patient population nor the general public. However, the multi-disciplinary research team does include diverse perspectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".