Decolonizing Health in Canada: A Manitoba First Nation Perspective
Bibliographic record
Abstract
Abstract Background This paper focuses on a longitudinal research program in Manitoba, Canada, by the Innovation Supporting Transformation in Community-Based Research Project (iPHIT) to learn from First Nations across the province that have developed effective community-based primary healthcare models. The research is relevant and timely as First Nations across the country, and Indigenous populations globally, work towards improvements in population health and health equity via critical analysis and restructuring of health services. The purpose of the paper is to deepen an understanding of decolonization as it is defined within the communities, as a central aspect of health restructuring. Methods The study is a qualitative, grounded theory analysis, which is a constructivist approach to social research that allows for generation of theory in praxis, through interactions and conversations between researchers and research participants. Findings are based on 183 in-depth interviews and eight focus group discussions with participants from 8 Manitoba First Nation communities. The study was designed to understand strengths, limitations and priorities of primary healthcare strategies and frameworks of the communities. The iPHIT team was an active collaborative partnership between the First Nation communities, First Nation Health and Social Secretariat of Manitoba, and the University of Manitoba. The First Nation partners led in all aspects of the research, from development to implementation, data collection, analyses, and dissemination. Respected Elders from the communities also guided in appropriate research and engagement protocols. Results Data was coded and then grouped into 4 interconnecting themes. These are: (1) First Nation control of healthcare, (2) traditional medicine and healing activities, (3) full community participation, and (4) moving out of colonization involves cleaning up and moving beyond the mess that colonization has inflicted. Conclusion Decolonizing health involves a taking back of Indigenous wisdom and traditional activities; connections to the land, resources; intra- and inter-community relationships. Participants emphasized the value of full community engagement with respect to inclusion of different interpretations of and experiences in the world, highlighting creation of a shared vision. The study focused on First Nation community experiences and interests in Manitoba specifically, though the data may be applicable to national and global decolonization efforts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.042 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".