Survey on Aboriginal issues within Canadian medical programmes
Bibliographic record
Abstract
INTRODUCTION: Medical programmes across Canada recognise the increased need for Aboriginal doctors. This study aimed to determine Canadian medical programmes' commitment towards Aboriginal health issues, recruitment, admission policies, educational opportunities and support offered to Aboriginal medical undergraduate students. METHODS: Medical school websites were initially reviewed to identify recruitment initiatives, admission policies and curriculum activities related to Aboriginal health. A questionnaire was sent to each dean of medicine to collect additional data on the programmes' recruitment strategies, admission policies, educational opportunities and the type of support offered to Aboriginal medical students. RESULTS: Sixteen medical programmes completed the questionnaire (return rate = 100%). There were 56 Aboriginal medical students enrolled across these medical programmes. More than 75% of students were completing their training in a western province. Over half of the medical programmes had recruitment initiatives and admission policies specific to Aboriginal applicants and the majority reported that their programmes' curricula included learning objectives specific to Aboriginal health. Most offered lectures and training opportunities to medical students and few offered core curriculum activities focusing on Aboriginal health. DISCUSSION: This descriptive paper offers a snapshot of initiatives across medical programmes aimed at increasing the number of Aboriginal applicants and medical students and at supporting their journeys towards the attainment of medical degrees. More research is needed to evaluate these initiatives' effectiveness. The results of such studies would not only provide needed information aimed at meeting the specific health needs of Aboriginal people, but may also contribute towards the laying of a framework to help narrow the gaps that exist within health care delivery to other minority groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".