[Health resources allocation in Canada provinces: the role of indicators of health needs].
Bibliographic record
Abstract
In an attempt to limit their health care expenditures Canadian provinces have strengthened the necessity to allocate health care resources according to their population needs. The difficulties and limitations of the needs-based approach are explored. First, indicators of population needs for health care were introduced into a formula of resource allocation for hospital-based services in England in the late 1970. Secondly, there are broad similarities between both the philosophy and resource allocation strategies of Canada and Britain. Thirdly, the main definition of a needs indicator is to measure the level of equity- or inequity-in the distribution of health care resources between regions. Fourthly, a needs indicator, as least as developed by the Canadian provinces, concerns general and specialized services that should be found in each of their regions. Fifthly, a needs indicator constitutes a tool for the calculation of a capitation rate. Finally, future research should focus on parameters which are not an integral part of the allocation method, but which have a strong impact, in the attainment of regional equity such as administrative decisions that are taken when budgets are to be allocated or reduced between regions.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".