Development and evaluation of a hospital management practice rating scale
Bibliographic record
Abstract
Background: Lacking methods to quantify the inter-hospital variance in hospital management practice (HMP) is a bottle neck for research on HMP and quality of care. This study aims to quantify the inter-hospital variance in HMP by developing a novel rating scale of HMP and evaluating its feasibility, reliability and validity.Methods: Based on the theory of hospital management, we developed a HMP rating scale with 4 dimensions: Target, operations, performance and talent management. We used questionnaires to collect relevant information from the hospital director, the medical affairs director, the head of the department of cardiology, and a cardiologist. And we also requested a list of administration documents. For validation of the scale, we applied it to 101 hospitals that had participated in the Third Phase of the Clinical Pathways in Acute Coronary Syndromes Study (CPACS-3) in 2013 and repeated it in 2014.Results: The HMP rating scale includes 17 indicators and 47 sub-indicators in the four dimensions; 85% and 97% of hospitals responded to the first and second survey respectively. A high degree of the test-retest reliability for the overall score (ICC = 0.8) was found between the two time points. Both split-half and Cronbach’α coefficient of the overall score exceeded 0.85. Cumulative percentage of variance in all dimensions was above 60%, and factors extracted in each dimension were highly consistent with the designed indicators and sub-indicators. The overall HMP score was different between hospital groups with different revenues, patients’ hospital stays, and number of clinical pathways (All p values < .01).Conclusions: The HMP rating scale was demonstrated reliable, valid, and responsive, but future studies with larger sample size in different settings are needed to confirm the study findings.
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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 | 0.035 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".