Informing critical indigenous health education through critical reflection: A qualitative consensus study
Bibliographic record
Abstract
Objective: To examine experiences of anti-Indigenous racism in a Canadian medical school and inform the development of critical and action-oriented Indigenous health education necessary to pave the way for reconciliation within health systems. Design: A qualitative study conducted within a constructivist paradigm which involved (1) semi-structured interviews with students, faculty and staff at a Canadian medical school and (2) consensus-building/collaborative analytical sessions with an Indigenous advisory group and a non-Indigenous working group. Setting: Twenty-three semi-structured interviews were completed with students, staff and faculty working across a Canadian medical school. Results: Inductive coding generated 211 codes that were grouped into seven overarching thematic domains. By engaging in an iterative dialogue with the advisory and working groups, we deductively aligned the thematic analysis with faculty-level and institution-level Indigenous education strategies to ensure local relevance. Self-reflective statements were developed with the advisory group to guide areas for action and resulted in 18 statements with five-point Likert-type-style response options. Conclusion: The results of this study suggest that promoting self-reflexivity in health professional education can prompts educators to engage with Indigenous health curriculum and pedagogy; mentorship and role modelling; and accountability. Critically evaluating systemic injustices at an individual level enables educators to resist systemic oppression and create change in the spaces where they work.
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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 | 0.097 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".