Co-creating Simulated Cultural Communication Scenarios with Indigenous Animators: An Evaluation of Innovative Clinical Cultural Safety Curriculum
Bibliographic record
Abstract
BACKGROUND: Building on partnerships with Indigenous communities and with the support of the Northern Ontario School of Medicine, faculty created groundbreaking, authentic cultural immersion curriculum designed to foster culturally safe interpersonal skills and cultural understanding. However, structural barriers to the teaching of clinical communication skills for culturally safe care to Indigenous patients persisted. To address this challenge, faculty collaborated with Indigenous animators on the co-creation of a new teaching modality of Simulated Cultural Communication Scenarios. We evaluated student learning experience, the faculty teaching experience, the attainment of teaching goals, benefits, and areas for improvement for this approach. METHODS: We piloted 9 Simulated Cultural Communication Scenarios with 64 medical students and 17 tutors. We collected quantitative and qualitative data regarding their experiences and perceptions of the new curriculum. The quantitative data was statistically summarized, and the qualitative data was coded and thematically analyzed. RESULTS: The emergent themes indicate that co-created Simulated Cultural Communication Scenarios support the acquisition of culturally safe clinical skills because the modality fosters authentic, safe, context rich, and anti-oppressive patient dialogue with Indigenous animators. Recommendations for optimizing the sessions included ensuring tutors have a deep understanding of the significance of cultural safety in patient care. As the pedagogy is different from the familiar standardized clinical skills sessions, tutors and students benefit from education on the pedagogical approach. CONCLUSION: Simulated Cultural Communication Scenarios, co-created with cultural insiders and academic educators, represent an authentic education approach to teaching culturally safe clinical encounters. The findings contribute to our understanding of translating social accountability into the clinical setting.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".