Does Equity in Healthcare Use Vary
Bibliographic record
Abstract
For over 30 years, Canadian provinces have provided universal public insurance for hospital and physician care; however, evidence points to persisting socio-economic inequity in healthcare use. Because provinces hold the responsibility for planning and funding most publicly insured health services, there is some variation in health system characteristics. In the context of such variation, this study systematically inves-tigated equity in healthcare use across the provinces. Drawing on the 2003 Canadian Community Health Survey, the author applied the indirect standardization approach to create an index of needs-adjusted inequity in the probability, total and conditional number of GP, specialist, hospital and dentist visits. Results reveal some variation in inequity across provinces; however, national trends show pro-rich inequity in the probability of a GP, specialist and dentist visit, and no significant evidence of inequity in inpatient care. Aside from income, the main socio-economic factors associated with inequity are education, complementary insurance for prescription drugs and dental care and, in some cases, region of residence. When total (and conditional) number of visits are examined, the pro-rich inequity in GP care disappears in all provinces.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".