The Two Great Healing Traditions: Issues, Opportunities, and Recommendations for an Integrated First Nations Healthcare System in Canada
Bibliographic record
Abstract
The First Nations in Manitoba, Canada, are calling for active recognition and incorporation of holistic traditional healing and medicine ways and approaches by the mainstream healthcare system that has hitherto tended to ignore all but biomedical approaches. This request for recognition requires elaboration on areas of opportunity for collaboration that could positively influence both Indigenous and allopathic medicine. We discuss pathways to an integrated healthcare system as community-based primary healthcare transformation. A community-based participatory research approach was used to engage eight Manitoba First Nations communities. One hundred and eighty-three (183) in-depth, semi-structured key informant interviews were completed in all communities. Grounded theory guided data analysis using NVivo 10 software. We learned that increased recognition and incorporation of traditional healing and medical methods would enhance a newly envisioned funded health system. Elders and healers will be meaningfully involved in the delivery of community-based primary health care. Funding for traditional healing and medicines are necessary components of primary health care. An overall respect for Indigenous health knowledge would aid transformation in community-based primary health care. Recognition of and respect for traditional healing, healers, medicines, therapies, and approaches is also recommended as part of addressing the legacy and intergenerational impact of assimilative policies including Indian residential schools as the Truth and Reconciliation Commission of Canada has stated in its Calls to Action.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.021 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".