Lessons Learned and Pathways Forward Indigenous Medical Workforce Development in Canada since 2004
Bibliographic record
Abstract
The vision of the Indigenous Physicians Association of Canada is healthy and vibrant Indigenous nations, communities, families and individuals supported by an abundance of well educated, well supported Indigenous physicians working together with others who contribute to this vision with us. In our first 5 years of operation as the Indigenous Physicians Association of Canada we have focused our efforts on workforce development, building partnerships with the Association of Faculties of Medicine of Canada, the Royal College of Physicians and Surgeons of Canada, the 17 Canadian faculties of Medicine, and many national Aboriginal organisations. We have asked, and continue to ask ourselves, what is necessary to have a medical workforce that is competent to provide high quality culturally safe care to First Nations, Inuit and Metis people? On reflection, some of these things include: a strong national Indigenous physicians organisation; mentorship and peer support; recognition and realisation of shared responsibility for Indigenous health workforce development; adequate resources; and long term commitment. This paper discusses Indigenous health workforce development goals, the Canadian medical school landscape as is relevant to pursuing these goals, recent Indigenous health education initiatives, and some of the lessons we have learned and will apply as we move forward and seek to achieve our goals.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".