Exploring approaches to teaching Indigenous health curricula from the perspectives of faculty and residents
Bibliographic record
Abstract
Providing residency training on the health challenges Indigenous peoples face can enhance learners’ skills and understanding. However, to deliver socially accountable education that positively impacts patient care, the training must be tailored to meet the specific needs of patients and communities. This project aimed to identify strategies for enhancing the effectiveness of Indigenous medical curricula at the residency level by incorporating the insights and experiences of faculty and residents involved in Indigenous education.Method We employed a thematic analysis approach, utilizing purposeful sampling to recruit 21 faculty members and 19 residents engaged in Indigenous education from three Canadian universities. Data collection involved semi-structured 60-minute interviews, which were subsequently analyzed by the research team.Results We found three main themes: (1) Critical components of Indigenous curricula; (2) Curricular pedagogy; (3) Critical reflection of ongoing harms. Key findings emphasized the importance of continuous exposure to Indigenous curriculum content, starting with community engagement and cultural events, and progressing to collaboration with experienced healthcare professionals and training in cultural humility, anti-racism, and awareness of colonialism’s legacy and biases.Conclusions By incorporating the insights and experiences of faculty and residents engaged in Indigenous education, the curriculum can become more effective and better tailored to address the health needs of Indigenous patients and communities.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".