First Nations healing in the hospital : On the quest to implement indigenous healing in a clinical setting
Bibliographic record
Abstract
This thesis focuses on the interaction of so-called indigenous and Euro-American healing\ntraditions in one of the most formal institutional settings: the hospital. The setting for this\nstudy is the Canadian Prairie provinces of Saskatchewan and Alberta, and the main\nindigenous population are Plains First Nations.\nIn the study I wish to discover if indigenous healing practices are able to adapt to\na setting that is so central to the definition of settler states. I do so within a broader\nperspective that sets healing within a study of the decolonization process. The main\nargument is that part of the road to healing lies through the official institutions of the\nCanadian medical system and that it involves decolonization process for both the\nindigenous and the dominant society. The thesis asks why are hospitals settings being\nchosen today as the places to establish indigenous healing services and practices? To\nanswer this question the thesis employs qualitative interview data and a reading of the\nliterature.\nOne of the key answers is that the hospital context permits the community of\nbiomedical practitioners and the indigenous healers to interact. On the one hand, this\ninteraction is seen as an important step for the revalorization and formal recognition of\nindigenous knowledge, and as determinant for the preservation and survival of it. On the\nother hand the field research shows that aboriginal patients feel extremely vulnerable\nwhen hospitalized and that the integration of indigenous healing within hospitals would\nimprove the quality health care.\nDespite these strong answers, the project remains explorative. The conclusions\nshow that there is no simple answer for how these two traditions can come together. One\nof the main reasons is that this process of implementation is at the very beginning. It\nshows as well that not all healers think that this is a good idea, and are worried about the\nexpropriation and integrity of the knowledge. Some questions remain inconclusive and\nfurther research will be necessary in order to give further answers
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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 | 0.054 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.063 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".