Comparing patient reports about hospital care across a Canadian-US border
Bibliographic record
Abstract
OBJECTIVE: To compare patient reports about hospital care between western New York State and southern Ontario using a random intercept model. METHOD: Cross-sectional survey of 3923 patients who received medical or surgical care between August and October 2004 at 28 hospitals (14 hospitals per jurisdiction). Thirty-five questions were combined to calculate eight indicators with scores ranging from 0 to 100 (best care experience). For each indicator, a model was built where the region (western New York vs. southern Ontario) was included as a fixed effect with hospital as random within region. A number of patient characteristics were also included as fixed effects. RESULTS: The effect of the region was statistically significant (P < 0.05) only for the models predicting the 'continuity and transition', 'involvement of family' and 'physical comfort' indicator scores. The differences were 10.66, 4.05 and -3.23 points, respectively. In all three models, the random intercepts were not statistically significant, indicating that the differences above did not vary by hospitals. The model predicting 'overall impression' scores, however, showed a random intercept statistically significant (P = 0.026). The individual-level explained proportion of variance ranged from 5.68 to 11.22%, and the hospital-within-region-level explained proportion of variance ranged from 2.19 to 52.28%. CONCLUSION: The difference observed on the 'continuity and transition' indicator might be the only one somewhat meaningful, and might be explained by health maintenance organization reimbursements' mechanisms and hospital quality improvement initiatives available in western New York, as well as by the fact that occupancy rates in western New York border the 60% compared with the 95% in southern Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".