Envisioning Indigenous and biomedical healthcare collaboration at Stanton Territorial Hospital, Northwest Territories
Bibliographic record
Abstract
Background: To improve the quality of care for Indigenous patients, local Indigenous leaders in the Northwest Territories, Canada have called for more culturally responsive models for Indigenous and biomedical healthcare collaboration at Stanton Territorial Hospital.Objective: This study examined how Indigenous patients and biomedical healthcare providers envision Indigenous healing practices working successfully with biomedical hospital care at Stanton Territorial Hospital.Methods: We carried out a qualitative study from May 2018 – June 2022. The study was overseen by an Indigenous Community Advisory Committee and was made up of two methods: (1) interviews (n = 41) with Indigenous Elders, patient advocates, and healthcare providers, and (2) sharing circles with four Indigenous Elders.Results: Participants’ responses revealed three conceptual models for Indigenous and biomedical healthcare collaboration: the (1) integration; (2) independence; and (2) revisioning relationship models. In this article, we describe participants’ proposed models and examine the extent to which each model is likely to improve care for Indigenous patients at Stanton Territorial Hospital. By surfacing new models for Indigenous and biomedical healthcare collaboration, the study findings deepen and extend understandings of hospital-based Indigenous wellness services and illuminate directions for future research.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".