Hospital efficiency and patient satisfaction
Bibliographic record
Abstract
The objective of this study was to investigate the relationship between efficiency and patient satisfaction for a sample of general, acute care hospitals in Ontario, Canada. A measure of patient satisfaction at the hospital level was constructed using data from a province-wide survey of patients in mid-1999. A measure of efficiency was constructed using data from a cost model used by the Ontario Ministry of Health, the primary funder of hospitals in Ontario. In accordance with previous studies, the model also included measures of hospital size, teaching status and rural location. Based on the results of this study, at a 95% confidence level, there does appear to be evidence to suggest that an inverse relationship between hospital efficiency and patient satisfaction exists. However, the magnitude of the effect appears to be small. Hospital size and teaching status also appear to affect satisfaction, with lower satisfaction scores reported among non-teaching and larger hospitals. This study did not find any evidence to suggest that hospital location (rural versus urban) or religious affiliation contributed to reports of patient satisfaction in any way not explained by the other measures included in the study. The findings imply that low patient satisfaction cannot be explained by excessive management concentration on efficiency. Managers should analyse some of the underlying causes of patient dissatisfaction before reconfiguring resources. It may be beneficial in larger hospitals to study the aspects of care that patients have reported they prefer in small hospitals.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".