Bibliographic record
Abstract
Countries with highly decentralized health systems such as Canada, the United States, and Spain, where the 'regions' hold the large share of responsibility for health care funding, provision and administration, face a trade off between decentralized policy making with regional inequalities. This study addresses one policy area in the Canadian context, equity in health care use, where differences in the formulation and implementation of policy may give rise to regional variations. Canada is a federal dominion of ten provinces and three territories. By 1972 all provinces and territories provided universal public insurance for hospital and physician care. Responsibility for the administration and delivery of most public health care services is held by the provinces in Canada. There is some variation across the country in health care financing, resource allocation and payment mechanisms, benefits, and supply of health services. This study quantifies the extent of provincial variation in health care use by income and determines its impact on equity. Income-related inequity in use of any physician, GP, specialist, inpatient and dental care is measured using the indirect standardization approach to calculating concentration indices. The needs-predicted level of health care use is compared with the observed distribution of health care use by income to generate an index of horizontal inequity (HI) that falls between -1 and 1 (negative values indicate a distribution of health care favouring lower income groups after standardizing for need, and vice versa). Results reveal some variation across provinces in inequity; however, national trends reveal pro-rich inequity in the probability of a GP, specialist, and dentist visit, and either no significant evidence of inequity or pro-poor inequity in inpatient care. When total number of visits are examined, the pro-rich inequity in GP disappears in all provinces, while specialist and dentist care remain pro-rich. A small island province - Prince Edward Island - comes out with relatively low levels of inequity, while two other Maritime provinces that are more remote and sparsely populated- Newfoundland and New Brunswick, appear to have greater inequity favouring the better off. Some of the variation across provinces may relate to differences in access barriers related to geography and complementary insurance coverage. While inequity differences are observed across the provinces, national trends suggest that despite being 'provincial' health systems, they are more similar than different.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 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".