Comparative analysis of health system performance in Montreal and New York: the importance of context for interpreting indicators
Bibliographic record
Abstract
Although eliminating financial barriers to care is a necessary condition for improving access to health services, it is not sufficient. Given the contrasting health systems with regard to financing and organization of health insurance in the United States and Canada, there is a long history of comparing these countries. We extend the empirical studies on the Canadian and US health systems by comparing access to ambulatory care as measured by hospitalization rates for ambulatory care sensitive conditions (ACSC) in Montreal and New York City. We find that, in New York, ACSC rates were more than twice as high (12.6 per 1000 population) as in Montreal (4.8 per 1000 population). After controlling for age, sex, and number of diagnoses, significant differences in ACSC rates are present in both cities, but are more pronounced in New York. Our findings are consistent with the hypothesis that universal, first-dollar health insurance coverage has contributed to lower ACSC rates in Montreal than New York. However, Montreal's surprisingly low ACSC rate calls for further research.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".