The health services use among older Canadians in rural and urban areas. Social and Economic Dimensions of an Aging Population Research Paper 178
Bibliographic record
Abstract
Although universal health care is one of the pillars of Canadian society, the rising cost of services has resulted in the relocation and redistribution of funding and services between rural and urban areas. While most econometric analyses of health service use in Canada include broad controls by province and rural/urban status, there has been little econometric work that has focused specifically on geographical variation in health service use. Using the 2002-03 wave of the Canadian Community Health Survey, we examine the determinants of a range of health services use by older Canadians across a range of urban and rural areas of residence. The regression analysis suggests two general conclusions: 1) health service use is lower among older residents of rural areas in terms of visits to a GP, to a specialist and to a dentist compared to residents of urban core CMA/CAs, ceteris paribus, but there are no significant differences in hospital nights; and 2) these results are robust across a range of specifications that control for demographic characteristics, socio-economic status, private health insurance, and physical health. However, the magnitude of the estimated differences is small. In addition, self-reported incidence of unmet healthcare needs overall shows no systematic variation across rural and urban areas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".