Curriculum Indigenization in oral health professions’ education worldwide: A scoping review
Bibliographic record
Abstract
OBJECTIVE: To explore the literature on Indigenous content within the oral health professions' education curricula. METHODS: This scoping review included all types of literature on oral health care educational programs on Indigenous content, following the JBI (Joanna Briggs Institute) methodology. An initial search using "Indigenous," "education," and "oral health" as keywords informed a full search strategy for MEDLINE, CINAHL, Embase, Scopus, ERIC, EPPI, MedEdPORTAL, Google Scholar, ProQuest Dissertations and Theses Global, Australian Government Department of Health, and Australian Indigenous HealthInfoNet. The search included literature available until November 1, 2023, irrespective of language. Two reviewers independently screened the studies, and data were extracted and presented in tabular and narrative summary formats. RESULTS: A total of 948 records were identified, and 101 studies were chosen for full-text review. Twenty-three studies met the criteria for data extraction. Of all studies, 95.6% were published between 2007 and 2021, mostly from Australia and New Zealand. The most frequently covered content included Indigenous culture, followed by history, Indigenous oral health, and Indigenous Peoples' health. Rural and clinical placements were the most employed delivery methods, and evaluation surveys were the most employed assessment technique. Barriers to delivering an Indigenous curriculum included students' disinterest and limited interaction with Indigenous communities, while facilitators included cultural immersion and supportive mentorship. CONCLUSION: Despite progress in integrating Indigenous content into oral health education, challenges persist. Prioritizing Indigenous perspectives, community partnerships, and standardized assessment tools is needed. Future research should focus on long-term impacts and best practices for Indigenous curriculum development and delivery.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".