Implementing Cultural Safety and Anti-Racism Training in Master of Public Health Curricula: A Multi-Institutional Case Study and Practical Self-Assessment Tool
Bibliographic record
Abstract
Racism and colonialism operate as social determinants of health, contributing to inequities among Indigenous Peoples. To counteract these inequities, national and global calls to action have emphasized the need for cultural safety and anti-racism within healthcare. Higher education plays a crucial role in preparing an equipped workforce and shaping professional culture, making it essential to embed cultural safety and anti-racism training into the learning pathways of future health professionals. This study examined the determinants shaping the uptake and implementation of cultural safety and anti-racism training in Master of Public Health (MPH) programs across three universities within one Canadian province. Data were collected through key informant interviews, focus groups, and document analysis. The Consolidated Framework for Implementation Research (CFIR) guided comprehensive analysis of the barriers and facilitators influencing how these training interventions are being put into practice. Findings highlighted similarities and variations in cultural safety and anti-racism training approaches across MPH curricula, highlighting the evolving nature of this training. The study underscores the need for multi-level action that addresses individual, institutional, and systemic challenges, while leveraging existing strengths. The significance of this research lies in its potential to inform curriculum reform, pedagogical practice, policy development, and professional culture in public health and related disciplines. To support academic units, a self-assessment tool developed from the findings offers a structured framework to reflect on and enhance efforts in implementing cultural safety and anti-racism training.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 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".