Health Care System Transformation in Rural and Indigenous Communities
Bibliographic record
Abstract
This dissertation is about Indigenous and settler health, wellbeing and health policy in rural Manitoba. Across Canada, both sweeping and incremental provincial health system changes have profound effects on marginalized communities facing existing health inequities, including rural settler and Indigenous peoples. Increasingly, the centralization of provincial health care systems has led to the elimination of health services within rural settings. The research I present in this dissertation arose from advocacy efforts in a rural community in southwest Manitoba. The community sought representation and recognition in health decision-making in the midst of the largest health care system transformation in provincial history and called for local research production. The community-led study grew to the larger inquiry and analysis presented here, including a First Nation and Métis community, which were both affected by the transformation. The objectives of this dissertation were to analyze the ideas and structures used to inform provincial decisions, and to understand community experiences of rural health care before and during system transformation. As a settler researcher, I undertake an anti-colonial, strengths-based, community-engaged approach to research, developed through ongoing relationship with the community. In addition to a critical thematic analysis of key policy documents, semi-structured interviews were conducted with settler, First Nations, and Métis community members and service providers regarding their experiences within the health care system, their perceptions of change and the impacts of transformation on health and wellbeing. Arising themes include the importance of relational health care relationships in determining wellbeing, and the sense that the government undertook dehumanized decision-making in developing and implementing health system change. This dissertation concludes with policy recommendations for provincial governments, including the prioritization of community voices, and the visibility and involvement of rural and Indigenous peoples in health system decision-making.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".