Decolonizing health in Canada: A Manitoba first nation perspective
Bibliographic record
Abstract
INTRODUCTION & BACKGROUND: Global persistence of health inequities for Indigenous peoples is evident in ongoing discrepancies in health and standards of living. International literature suggests the key to transformation lies in Indigenous efforts to control Indigenous health and healthcare. Previous authors have focused upon participation, structural transformation, and culturally appropriate healthcare recognized as a political right as fundamental tenets of Indigenous control. Contextualizing Indigenous health and wellness falls within a growing discussion on decolonization - a resituating of expertise that privileges Indigenous voice and interests. METHODS: The study is a qualitative, grounded theory analysis, which is a constructivist approach to social research allowing for generation of theory in praxis, through interactions and conversations between researchers and participants. One hundred eighty-three interviews with additional focus groups were held between 2013-15 in eight Manitoba First Nation communities representing different models of health delivery, geographies, accessibilities, and Indigenous language groups. Community research assistants and respected Elders participated in data collection, analysis and interpretation. Line-by-line coding and constant comparative method led to the discovery of converging themes. FINDINGS: Ultimately four main themes arose: 1) First Nation control of healthcare; 2) traditional medicine and healing activities; 3) full and meaningful community participation; and 4) cleaning up impacts of colonization. Joint analyses and interpretation of findings revealed substantial evidence that communities were looking profoundly into problems of improperly delivered services and health inequities. Issues were consistent with those highlighted by international commissions on reconciliation, health, Indigenous rights and liberties. To those documents, these findings add ground upon which to build the transformative agenda. RESULTS & DISCUSSION: Communities discussed the need for creation of protocols, constitution and laws to ensure growth of a decolonizing agenda. Inclusive to the concept are holistic, preventative, traditional health perspectives, and Indigenous languages. Colonization impacts were of critical concern and in need of undoing. Sharing of social and political efforts is seen as pivotal to change and includes all members of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.038 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".