Root Medicine: \nConfronting Indigenous Segregation and Building Partnership in Qu’Appelle Healthcare, 1870-1950
Bibliographic record
Abstract
In recent decades, oral history and storywork have transformed the study of Indigenous health histories. Through a desire-based research framework, this study aims to foreground the many ways by which First Nations reserve communities have continuously asserted self-determination through practices of holistic, land-based wellness in Treaty Four territory. The two-part study begins in the context of the All Nations Healing Hospital in Fort Qu’Appelle, Saskatchewan. With a blended approach of clinical Western medicine and a robust offering of traditional Indigenous therapeutics, the hospital reflects the diverse cultural protocols of the Nêhiyawak /ᓀᐦᐃᔭᐤ (Plains Cree), Anihšināpē/Anishinaabe (Saulteaux), Nakoda/Assiniboine, Lakota, and Dakota. Through the voices of three co-researchers, narratives of hope and empowerment are emphasized with on-the-ground examples of healing. Building from these rooted understandings of Indigenous wellness, the study then explores the development of segregated healthcare and biomedicine in the settlement period, 1870-1950. The fight against tuberculosis in the Qu’Appelle region was headed by Doctor R. G. Ferguson, whose activities in the Fort San Sanitorium and Fort Qu’Appelle Indian Hospital reveal ideologies which prevented, and may still prevent, Indigenous-led healthcare collaboration. While Western attitudes of civilization and medical progress have asserted decidedly linear histories, Indigenous perspectives on health present a cyclical and highly adaptive knowledge base. In this way, the present success of the All Nations Healing Hospital is not simply a departure from oppressive medical systems of the past, but an ongoing reflection of cultural continuity and embodied governance in health and healing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".