How do hospitalization experience and institutional characteristics influence inpatient satisfaction? A multilevel approach
Bibliographic record
Abstract
Over the last several years, interest in benchmarking health services' quality--particularly patient satisfaction (PS)--across organizations has increased. Comparing patient experiences of care across hospitals requires risk adjustment to control for important differences in patient case-mix and provider characteristics. This study investigates the individual-level and organizational-level determinants of PS with public hospitals by applying hierarchical models. The analysis focuses on the effect of hospital characteristics, such as self-discharges, on overall evaluations and on across hospital variation in scores. Sociodemographics, admission mode, place of residence, hospitalization ward and continuity of care were statistically significant predictors of inpatient satisfaction. Interestingly, it was observed that hospitals with a higher percentage of Patients Leaving Against Medical Advice (PLAMA) received lower scores. The latter result suggests that the percentage of PLAMA may provide a useful measure of a hospital's inability to meet patient needs and a proxy indicator of PS with hospital care.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".