Adolescent receptiveness to dentist involvement in COVID-19 and HPV vaccination
Bibliographic record
Abstract
OBJECTIVES: Human papillomavirus (HPV) and COVID-19 can be prevented and mitigated by vaccines. Few studies have focused on dentists' role in vaccine decision making, and even fewer have explored adolescent perspectives; a target population for both vaccinations. This study aimed to address this gap with a focus on whether opinions vary between diseases. METHODS: We administered a validated cross-sectional survey to adolescent patients (11-19 years) in an orthodontic clinic in Vancouver, British Columbia from July-August 2023. The survey included questions pertaining to patient background, vaccine history and knowledge, and dentists' roles in vaccination education, discussion, and administration. Responses were compiled and analyzed to determine differences between vaccines and across demographic groups. RESULTS: Adolescents surveyed (n=93) overall agreed with dentist involvement in COVID-19 and HPV vaccines, with variability according to disease and dentist role. Comfort with dentist-administered vaccines was higher for COVID-19 (60 %) than HPV (37 %, p<0.05). There was a significant knowledge difference, with 85 % aware that the COVID-19 vaccine can prevent severe illness, but only 22 % aware that the HPV vaccine can prevent oropharyngeal cancer (p<0.05). Patients showed overall willingness to discuss COVID-19/HPV vaccines with dentists (58 % and 49 %) and less agreement that dentists were qualified to educate about COVID-19/HPV vaccines (43 % and 37 %). CONCLUSIONS: Findings indicate mixed adolescent perception of including dentists in vaccinations, with higher comfort around COVID-19 over HPV vaccines. Openness to discussion may present an opportunity for dentists to expand scope of practice into additional education, particularly around HPV and its connection with oropharyngeal cancer.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".