The Practices and Challenges of Indigenizing Dental and Dental Hygiene Education in Canada
Bibliographic record
Abstract
OBJECTIVES: To explore the extent to which Indigenous content is taught in dental and dental hygiene curricula across Canada and to identify their objectives, delivery methods, barriers, and facilitators. METHODS: A descriptive cross-sectional design was utilized via an anonymous survey developed using the Qualtrics platform. The survey was distributed to faculty members from all 10 accredited dental and 35 dental hygiene programs in Canada. The survey included 29 items focusing on demographic characteristics, Indigenous teaching, methods of delivery, assessment techniques, barriers, and facilitators. Descriptive analysis was conducted using SPSS software version 29.0. RESULTS: Responses were received from 34 programs; 90% (#9) of the undergraduate dental programs and 71% (#25) of the dental hygiene programs participated. Of the programs surveyed, 94.1% (n = 32) include Indigenous content. On average, 12.94 ± 7.44 h was dedicated to teaching such content. The most common delivery method was didactic format (88%), and the most frequently covered topics were history and Indigenous people's health, each covered in 79.4% of programs. Major barriers identified were overcrowded curricula (83.3%) and faculty shortages (58.3%), while key facilitators included supportive institutional policies (71.4%) and engagement with Indigenous experts (61.9%). CONCLUSIONS: The study reveals that most Canadian dental and dental hygiene programs that responded included Indigenous content within their training. However, barriers such as overcrowded curricula and faculty shortages persist. Supportive institutional policies and the involvement of Indigenous professionals are vital for effective curriculum indigenization.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".