Reducing Indigenous Oral Health Inequalities: A Review from 5 Nations
Bibliographic record
Abstract
Indigenous populations around the world experience a disproportionate burden in terms of oral diseases and conditions. These inequalities are likely due to a complex web of social determinants that includes poverty, historical consequences of colonialism, social exclusion, government policies of assimilation, cultural annihilation, and racism in all its forms (societal, institutional). Despite documented oral health disparities, prevention interventions have been scarce in Indigenous communities. This review describes oral health interventions and their outcomes conducted for Indigenous populations of the United States, Canada, Brazil, Australia, and New Zealand. The review includes research published since 2006 that are available in English in electronic databases, including MEDLINE. A total of 13 studies were included from the United States, Canada, Brazil, and Australia. The studies reviewed provide a wide range of initiatives, including interventions for prevention and treatment of dental disease, as well as interventions that improve oral health knowledge, behaviors, and other psychosocial factors. Overall, 6 studies resulted in improved oral health in the study participants, including improvements in periodontal health, caries reduction, and oral health literacy. Preferred intervention methodologies included community-based research approaches, culturally tailored strategies, and use of community workers to deliver the initiative. Although these studies were conducted with discrete Indigenous populations, investigators reported similar challenges in research implementation. Recommendations for future work in reducing oral health disparities include addressing social determinants of health in various Indigenous populations, training future generations of dental providers in cultural competency, and making Indigenous communities true partners in research.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".