Understanding Perceptions of Non-Indigenous Medical Educators’ Professional Competency for the Integration and Delivery of Indigenous Health Curriculum in Medicine
Bibliographic record
Abstract
The Canadian medical education system is to increase curricula on Indigenous health as outlined in the Truth and Reconciliation Commission’s (TRC) Call to Action #24; medical schools need instructors with cultural competency. As most instructors are non-Indigenous Medical Educators (NIMEs), medical educators urgently need to understand what it means to be culturally competent within Indigenous health and engage with the TRC Calls to Action #24, which states: “We call upon medical and nursing schools in Canada to require all students to take a course dealing with Aboriginal health issues... This will require skills-based training in intercultural competency, conflict resolution, human rights, and anti-racism (TRC, 2015, 3).” This research examines what constitutes competency in teaching Indigenous health curricula in medical education. Using critical race theory for analysis, three areas are explored: 1. understanding competency; 2. the role of Indigenous health in medicine; and 3. educator and learner perspectives. One-to-one interviews were conducted with Indigenous learners and medical educators, frontline non-Indigenous medical educators and senior leadership from across Canada’s medical schools. The data allowed for a robust understanding of what competency to teach Indigenous health means when the participants in systems of Indigenous health curricula share their views on NIMEs and account for how Indigenous and Western knowledge often difer in conceptualization and expression. The analysis provided recommendations for NIME training and a snapshot of NIME professional competencies from their perspectives and those of people receiving their teaching. From this research, an initial framework of ethical standards for the teaching of Indigenous health was developed. This framework can be instrumental in developing territorial-based standards between medical schools and local Indigenous communities in which medical schools are situated. It can also support medicine’s regulatory, policy, and academic bodies of medicine in addressing the TRC Call to Action #24.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".