Determinants of dentists’ readiness to assess HPV risk and recommend immunization: A transtheoretical model of change-based cross-sectional study of Ontario dentists
Bibliographic record
Abstract
OBJECTIVES: To evaluate dentists' readiness to assess the history of human papilloma virus (HPV) infections and recommend immunization among their patients. MATERIALS AND METHODS: A link to a self-administered questionnaire was emailed to Ontario dentists. Dentists' readiness and its determinants were assessed based on Transtheoretical Model's 'stages' and 'processes' of change, respectively. Based on their current practices, dentists were either assigned to 'pre-action' or 'action+' stages. RESULTS: Of the 9,975 dentists contacted, 932 completed the survey; 51.9% participants were in action stage to assess the history of HPV infections and 20.5% to recommend immunization. Internationally-trained and those whose office's physical layout was not a concern to discuss patients' sexual history were more likely to assess the history. Dentists with higher knowledge about HPV vaccines, not concerned about the HPV vaccine safety, comfortable discussing sex-related topics with patients, or willing to exceed their scope of practice were more ready to recommend HPV immunization to their patients. CONCLUSION: Improving Ontario dentists' knowledge and communication skills and changing their self-perceived role regarding HPV infections and vaccination can increase their capacity to minimize the burden of HPV infections.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".