Envisioning the Future of Aboriginal Health Under the Health Transfer Process
Bibliographic record
Abstract
The Canadian government, and many Aboriginal communities, are committed to formally transferring varying aspects of governance responsibilities from federal hands to Aboriginal ones. These transfers take various forms, from creating Aboriginal political bodies with broad sets of governance powers, as was the case with the Nisga'a Treaty of 2000, to more partial transfers of specific powers or responsibilities, or types of responsibilities. One core transfer area is public health programming, for which there are specific and highly developed initiatives dating back to around 1989. Although it is expected that these initiatives will, overall, have very positive effects for improving the health of Aboriginal Canadians, there are many difficulties which are likely to emerge or be perpetuated under these transfers. There has been limited analysis of these difficulties to date. This paper first briefly describes the history of health transfer initiatives, and the policies which currently shape transfer agreements. After establishing this general platform, the paper then takes up the challenge of querying whether improvements to health status actually follow these forms of transferred control. The point of asking this question, as James Waldram, Ann Herring and Kue Young suggest, is not to undermine the efforts of Aboriginal communities to ameliorate their often poor living conditions, but to generate an analysis of how law, policy, and jurisdictional assignment impede or facilitate the success of such initiatives, and so gather insight into how to make improvement more likely. This paper considers some existing gaps or problems in Aboriginal public health which are likely to be perpetuated despite the transfer of control over some aspects of these problems, as well as some gaps related to health which may emerge in transfer communities. It then turns to identifying some aspects of health which are likely to improve in the coming years with increased Aboriginal control. The analysis in this paper is obviously a selective one: there are many other "gaps" which could have been included. As such, it is intended to contribute to the initiation of a broader conversation about the future of Aboriginal health under the health transfer process.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.062 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".