Using interviews to construct and disseminate knowledge of oral health policy
Bibliographic record
Abstract
OBJECTIVES: Policymakers worldwide are challenged by the problem of oral health inequities. The goal of an interprovincial partnership in Canada was to guide policy aimed at improving the oral health of vulnerable populations. Insights regarding barriers and enablers to developing such policy in one province (Newfoundland & Labrador, Canada) were required to enhance collaboration between decision makers and researchers and to contribute to the evidence informing policy development. METHODS: Snowball technique identified fourteen key informants. Semistructured audio-recorded interviews were conducted in person or by telephone. Two researchers independently conducted the analyses of the transcribed interviews, one using NVivo software and the second, manual coding. Triangulation of the analyses confirmed the findings. RESULTS: Agreement between the two approaches showed that most key informants believed that oral health is an important policy issue; however, most felt it was not a high priority among the general public and most were unable to articulate the policy process. Barriers to oral health becoming a governmental priority were related to resource allocation and to poor communication among some groups including dentists and dental hygienists. Current government programmes and initiatives were praised but considered weak in health promotion strategies. Recommendations for enhancing oral health priority varied. CONCLUSIONS: Attention to the methodological considerations of qualitative research enhanced the credibility of the method and confidence in the findings. Leveraging of existing programmes and improving communication were recommended to contribute to raising the priority of oral health within the government, thereby increasing their commitment to address oral health care, particularly for vulnerable populations.
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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 | 0.083 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".