The Use of Fluoride Varnish in Primary Care in Ontario: A Qualitative Study.
Bibliographic record
Abstract
OBJECTIVES: Fluoride varnish (FV) has been shown to prevent dental caries. Physicians and nurses may be ideally situated to apply FV during well-child visits. Currently, public health units across Ontario have been successfully piloting this intervention. Yet, challenges remain at both the political and practice levels. The objectives of this research were to understand the perspectives of key stakeholders on making FV application a routine primary care practice in Ontario and to consider the potential enabling factors and barriers to implementation. METHODS: In this qualitative study, 16 key stakeholders representing medicine, nursing, dentistry, dental hygiene, public health and government were interviewed. Interview data were transcribed and coded, and a conceptual framework for implementing change to daily health care practice was used as a guide for thematic analysis. RESULTS: Our findings suggest that there is an opportunity for interdisciplinary care when considering children's oral health. There is also motivation and acceptance of this specific intervention across all fields. However, we found that concerns related to funding, knowledge and interprofessional relationships could impede implementation and limit any potential short- or mid-term window for meaningful policy and practice change. CONCLUSION: With respect to introducing FV into medical practice for children under 5 years of age, the many factors required to implement immediate change are arguably not in alignment. However, policymakers and practitioners are motivated and have identified opportunities for change that may form the foundation for this program in the future.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".