Organizational performance impacting patient satisfaction in Ontario hospitals: a multilevel analysis
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction in health care constitutes an important component of organizational performance in the hospital setting. Satisfaction measures have been developed and used to evaluate and improve hospital performance, quality of care and physician practice. In order to direct improvement strategies, it is necessary to evaluate both individual and organizational factors that can impact patients' perception of care. The study aims were to determine the dimensions of patient satisfaction, and to analyze the individual and organizational determinants of satisfaction dimensions in hospitals. METHODS: We used patient and hospital survey data as well as administrative data collected for a 2008 public hospital report in Ontario, Canada. We evaluated the clustering of patient survey items with exploratory factor analysis and derived plausible dimensions of satisfaction. A two-level multivariate model was fitted to analyze the determinants of satisfaction. RESULTS: We found eight satisfaction factors, with acceptable to good level of loadings and good reliability. More than 95% of variation in patient satisfaction scores was attributable to patient-level variation, with less than 5% attributable to hospital-level variation. The hierarchical models explain 5 to 17% of variation at the patient level and up to 52% of variation between hospitals. Individual patient characteristics had the strongest association with all dimensions of satisfaction. Few organizational performance indicators are associated with patient satisfaction and significant determinants differ according to the satisfaction dimension. CONCLUSIONS: The research findings highlight the importance of adjusting for both patient-level and organization-level characteristics when evaluating patient satisfaction. Better understanding and measurement of organization-level activities and processes associated with patient satisfaction could contribute to improved satisfaction ratings and care quality.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".