COVID-19 incidence and vaccination rates among Canadian dental hygienists.
Bibliographic record
Abstract
Background: Oral health care settings potentially carry a high risk of cross-infection due to close contact and aerosol-generating procedures. There is limited evidence of the impact of COVID-19 among dental hygienists. This longitudinal study aimed to 1) estimate COVID-19 incidence rates among Canadian dental hygienists over a 1-year period; and 2) estimate vaccination rates among Canadian dental hygienists. Methods: A prospective cohort study design was used to collect self-reported COVID-19 status from 876 registered dental hygienists across Canada via an online baseline survey and then 6 follow-up questionnaires delivered between December 2020 and January 2022. Bayesian Poisson and binomial models were used to estimate the incidence rate and cumulative incidence of self-reported COVID-19. Results: The estimated cumulative incidence of COVID-19 in dental hygienists in Canada from December 2020 to January 2022 was 2.39% (95% CrI, 1.49%-3.50%), while the estimated cumulative incidence of COVID-19 in corresponding Canadian provinces was 5.12% (95% CrI, 5.12%-5.13%) during the same period. At last follow-up, 89.4% of participants self-reported that they had received at least 1 dose of a COVID-19 vaccine. Conclusion: The low infection rate observed among Canadian dental hygienists between December 2020 and January 2022 is reassuring to the dental hygiene and general community.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".