Indigenous oral health equity: The path forward.
Bibliographic record
Abstract
Objective: This narrative review explores current research on Indigenous oral health equity (OHE) and proposes a framework and strategies to guide registered dental hygienists in addressing OHE and health disparities experienced by Indigenous Peoples. Methods: An electronic literature search was performed using PubMed and Google Scholar. Full-text, peer-reviewed articles, written in English, and published in Canada within the last 10 years were selected for the review. The relevant grey literature was also included to ensure Indigenous perspectives relating to oral health inequity in Canada were considered and to increase the utility of the proposed strategies. Included articles were analyzed for themes. Results: Ten articles and 6 publications from the grey literature met the inclusion criteria. Discussion: The framework has 4 elements: policy driven changes to dental hygiene regulation, relationship building and allyship, workforce development, and research and education. Strategies within the framework can assist dental hygienists in adapting their approaches to oral health care delivery with Indigenous Peoples and promote OHE by providing inclusive and culturally safer care. Conclusion: Indigenous people throughout Canada have the right to safe oral health care, and dental hygienists can use this framework and these recommendations to promote Indigenous OHE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".