Key Challenges for Indigenous Peoples of Canada in terms of Oral Health Provision and Utilization: A Scoping Review
Bibliographic record
Abstract
Background: The oral health of Indigenous peoples in Canada is lacking compared with their non-Indigenous counterparts. This scoping assessment aimed to investigate the obstacles of providing and using oral healthcare among Indigenous peoples in Canada. Methods: The scoping review took place between December 15, 2021 and January 10, 2022. Five key databases were examined: PubMed, Scopus, ISI Web of Science, Embase, and PROQUEST. The data were analyzed using NVIVO software to facilitate understanding of the major themes, subthemes, and codes provided. Results: Seven major themes and eighteen subthemes were identified as impacting the oral health provision and utilization of Indigenous peoples in Canada. The major themes are individual characteristics, affordability, availability, accessibility, accommodation, acceptability, and public or government policy. Thus, to improve the oral health of the Indigenous peoples in Canada, an integrated approach is required to address these obstacles. Conclusions: To address the oral health disparities among Indigenous peoples in Canada, policymakers should adopt an integrated approach.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".