Learning about Indigenous Health Immersion and Living with Elders in Northern Canada
Bibliographic record
Abstract
Introduction: Disparities in health outcomes exist between Indigenous and non-Indigenous people and between rural and urban populations. Non- Indigenous healthcare providers are often challenged by the effectiveness of the care they provide to Indigenous people. Training to provide culturally appropriate and safe care offers a potential solution. Aim: The aim of this paper is to describe how the Northern Family Medicine (Norfam) program of Memorial University in Canada was developed. It also discusses how learning about Indigenous health can address the physician human resource need and improve health outcomes for Indigenous people. Method: This paper describes our 18 years of experience of the Norfam program in providing Indigenous culture and health training for family medicine residents and medical students. Norfam has evolved to include learning through immersion in Indigenous communities, and land based experiential learning. Immersion includes working and learning from local health workers and community members. The land-based experience includes a bush walk to share the experience of living on the land with Elders, and to appreciate what the land means to them. Results: The development of the Norfam program has led to filling all the physician positions in our region. Conclusion: The Norfam training program in remote communities, with attention to the needs of the Indigenous population, is associated with meeting the physician human resource need and with improvement in health outcomes of the predominantly Indigenous population in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".