Collaborative and Systems Approach to Transforming Primary Health Care in Manitoba First Nations Communities
Bibliographic record
Abstract
The models of primary health care currently operating in First Nations communities are rooted in policies that were crafted without prior appropriate consultations. Many have continued to be applied even though they no longer adequately serve the needs of First Nations communities and people, if they ever did. Transforming primary health care will necessarily involve community- inclusive and self-determined reviewing of existing policies with a goal of implementing opportunities to update policies and models of care. This study was a partnership with university-based researchers, a First Nations health and social development entity separately established by a regional organization of First Nations Chiefs, and eight First Nations communities. A multi-pronged methodology was used in which five concurrent studies employing qualitative, quantitative, and case-study methods provided information on the primary health care experiences of First Nations and rural and remote communities. The program of research took a community-based participatory approach to engage participants in designing and carrying out data gathering while strengthening local capacity and encouraging long-term ownership of the process of research for change. Participating communities pointed out key setbacks to community- based primary health care, including differing models of care, jurisdictional complexities, funding that creates isolated programs within the same community, lack of promotion of cooperation among health care services, and a general acute approach to health care service delivery in the community. These barriers are both problems and opportunities for change. A borderless health care system that is jurisdictionally seamless and that promotes collaboration through cooperative funding models that reflect community priorities is recommended and advocated for all Manitoba First Nations communities.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".