Culturally Safe Practices in the Co-creation of Medical Education Curriculum with Indigenous Animators: Outcomes From an Indigenous Learning Circle
Bibliographic record
Abstract
OBJECTIVES: To explore the experiences of Indigenous patient actors who co-created and enacted Indigenous patient scenarios in collaboration with medical school faculty. We critically examine the structures and systems in a medical school that mediate cultural safety for Indigenous patient actors. The Truth and Reconciliation Commission of Canada has called on medical schools and healthcare institutions to help address the intergenerational harms inflicted on Indigenous people by the Indian residential school (IRS) system. Institutions are striving to incorporate cultural competency, conflict resolution, human rights, and anti-racism education into their curricula. However, the structural inequities within undergraduate, postgraduate, and continuing medical education practices must be identified and challenged to ensure that medical education is authentic and culturally safe for those involved in the development and delivery of the Indigenous health curriculum. To explore potential structural inequities in the co-creation process of simulated cultural communication scenarios (SCCS), the Indigenous animators at Debajehmujig Storytellers and collaborating faculty and professional staff at the Northern Ontario School of Medicine University (NOSM U) examined cultural safety in their curriculum design and delivery process. METHODS: We utilized the qualitative Indigenous research methodology of the Learning Circle to deconstruct the co-creation process and to explore the experience of cultural safety from the Indigenous animators' perspective throughout the curriculum design and delivery process. RESULTS: A framework for culturally safe co-creation practices with Indigenous people, rooted within Indigenous teachings of the Medicine Wheel, emerged from the qualitative data. CONCLUSIONS: This framework has the potential to guide the practice of culturally safe co-creation of Indigenous patient simulations in medical education and healthcare workplace learning. While the Medicine Wheel teachings are held by specific Indigenous nations, we anticipate that the results and recommendations of this study will apply to Indigenous co-creators and academic medical educators internationally.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".