<scp>COVID</scp>‐19 Vaccination in Canadian Dental Schools
Bibliographic record
Abstract
BACKGROUND: Oral Healthcare workers, including dental students, face a great risk of COVID-19 infection. High COVID-19 vaccination coverage is essential for a protected workforce. This study, which aims to document the COVID-19 vaccination experience among dental students and employees from Canadian dental schools during the COVID-19 pandemic, provides crucial insights that can significantly impact future vaccination strategies. METHODS: This study used data from a prospective cohort conducted between April 2021 and May 2022. We recruited 600 participants, including dental students, faculty, and support staff from 10 Canadian dental schools. Data were collected monthly from all subjects. Vaccination acceptance and vaccination time were assessed. Logistic regression models were performed to identify predictors of COVID-19 vaccine acceptance and late vaccination. In order to detect hesitation tendencies, descriptive statistics were used to observe the distribution of time to vaccination between age groups of employees and students. RESULTS: Out of 600 participants at baseline (70% female; average age 36 years old), 91% received at least one dose of the COVID-19 vaccine. No associations were found between sociodemographic factors and COVID-19 vaccine acceptance. Individuals aged 50-59 were less likely to delay the vaccination than most of our sample. Students presented more outliers for later vaccination times, particularly in younger age groups. CONCLUSION: High vaccination acceptance among dental students is crucial for promoting professionalism and influencing patients. Integrating vaccine advocacy into their education might enhance vaccination uptake in the general population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".