A mixed methods approach to obtaining health care provider feedback for the development of a Canadian pediatric dental caries risk assessment tool for children <6 years
Bibliographic record
Abstract
Introduction: Early childhood caries (ECC) is a chronic but preventable disease affecting young children worldwide. Many young children face access to care barriers to early preventive dental visits for a variety of reasons, which can increase their risk for ECC. Non-dental primary health care providers are well positioned to assist in assessing a child's risk for ECC by performing caries risk assessment (CRA). The purpose of this project was to report on primary health care provider and stakeholder feedback in order to refine a drafted CRA tool for Canadian children <6 years of age intended for use by non-dental primary health care providers. Methods: In this mixed methods project, we conducted six focus groups with primarily non-dental primary health care providers followed by a short paper-based survey to quantify preferences and feedback. Data were thematically and descriptively analyzed. Results: Participants' feedback on the drafted CRA tool included the need for it to be relatively quick to complete, easy and practical to score, easy to implement into practitioners' clinic schedules, and to include anticipatory guidance information to share with parents and caregivers. All participants (100%) welcomed a CRA tool. Many (85.4%) liked a layout that could be added to tools they already utilize. Most (73.2%) wanted the tool to be in colour, and many (90.2%) wanted the tool to include pictures. Conclusion: Non-dental primary health care providers informed the final development and layout of the newly released Canadian CRA tool. Their feedback resulted in a user-friendly CRA tool with provider-patient dynamics and preferences.
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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 | 0.134 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".