Recommendations for Integrating Caries Risk Assessment into Primary Care for Indigenous Children
Bibliographic record
Abstract
OBJECTIVES: This study aimed to identify strategies for implementing and integrating the Canadian Caries Risk Assessment (CRA) tool for preschoolers into the primary care of First Nations and Métis children in Manitoba, Canada, based on the perspectives of nondental primary care providers (NDPCPs). METHODS: An exploratory qualitative design was employed to gather insights from NDPCPs who provide care to Indigenous children aged <6 y. Fifty participants were purposefully recruited from 10 urban, rural, and remote communities across Manitoba. Data were collected through 8 focus groups and 12 in-depth key informant interviews conducted between April 2023 and September 2024. Interviews were transcribed verbatim and analyzed thematically via an inductive approach informed by a social constructivist framework. RESULTS: Four interrelated themes were identified by participants as central to CRA implementation and integration. Strengthening primary care systems involved training in fluoride varnish application, management endorsement, electronic medical record integration, standardized documentation, and incentives such as fee-for-service models. Building trust and culturally safe connections with Indigenous communities included establishing respectful relationships, embedding CRA into trusted programs, and addressing access barriers such as transportation and oral health supplies. Educating and engaging families focused on developing accessible educational materials and using trusted communication channels such as Facebook and local radio to improve oral health literacy. Advocating for policy changes involved calls for billing codes, sugar reduction policies, and CRA integration into existing well-child programs. CONCLUSION: NDPCPs in Manitoba are supportive of integrating the CRA tool into Indigenous pediatric primary care. Their recommendations offer a practical road map for CRA implementation, emphasizing systemic support, culturally responsive care, education, and policy alignment. These findings contribute to broader efforts to reduce oral health disparities and improve early childhood caries prevention in underserved populations.Knowledge Transfer Statement:Nondental primary care providers recommend integrating Caries Risk Assessment tools into Indigenous children's care through enhanced training, culturally safe engagement, and supportive policy development to address persistent oral health inequities.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".