The importance of place of residence in patient satisfaction
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of patients' place of residence on their evaluations of care, and to explore related policy implications. STUDY DESIGN: We used a conditional regression analysis of stratum matched case controls to examine whether place of residence of patients living in the Greater Toronto Area (GTA) or in Ontario outside of the GTA affects patient satisfaction with their experiences during hospitalization. SETTING: One hundred and six acute care hospitals located in the province of Ontario, Canada. PARTICIPANTS: A total of 101 683 Ontario residents hospitalized as inpatients between 1 October 2002 and 30 June 2004. MAIN OUTCOME MEASURES: Patient satisfaction indicators publicly reported in Ontario comprising patient perceptions of consideration, responsiveness, communication, and overall impressions, scored on a continuous scale from 1 to 100. RESULTS: Patients who lived outside Toronto were consistently more satisfied than patients who lived inside Toronto when both types of patients were hospitalized in Toronto (P < 0.0001). In contrast, patients who lived inside Toronto were usually and substantially more satisfied than patients who lived outside Toronto when they were hospitalized in facilities outside Toronto (P < 0.02). These findings were consistent after adjustment for several patient-level predictor variables: age, sex, self-assessed health status and number of hospital stays in the last 6 months. CONCLUSION: Findings suggest that where patients live has a small but potentially important impact on how they rate their care. Residence may therefore be considered when designing public reporting systems and pay-for-performance programs. Further attention to patient-level factors may be important to accurate and useful public reporting of patient satisfaction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".