The incidence of COVID-19 among dentists practicing in the community in Canada
Bibliographic record
Abstract
BACKGROUND: Oral health care settings carry a potentially high risk of causing cross-infection between dentists and patients and among dental staff members due to close contact and use of aerosol-generating procedures. The authors aimed to estimate COVID-19 incidence rates among Canadian dentists over a 6-month period. METHODS: The authors conducted a prospective cohort study of 644 licensed dentists across Canada from July 29, 2020, through February 12, 2021. An online questionnaire, adapted from the World Health Organization's Unity Studies protocols for assessment of COVID-19 risk among health care workers, was used to collect data on self-reported severe acute respiratory syndrome coronavirus 2 infections every 4 weeks. A bayesian Poisson model was used to estimate the incidence rate and corresponding 95% credible intervals (CIs). RESULTS: Median age of participants was 47 years; most participants were women (56.4%) and general practitioners (90.8%). Median follow-up time was 188 days. Six participants reported COVID-19 infections during the study period, giving an incidence rate of 5.10 per 100,000 person-days (95% CI, 1.86 to 9.91 per 100,000 person-days). The incidence proportion was estimated to be 1,084 per 100,000 dentists (95% CI, 438 to 2,011 per 100,000 dentists) and 1,864 per 100,000 people (95% CI, 1,859 to 1,868 per 100,000 people) in the Canadian population during the same period. CONCLUSIONS: The low infection rate observed among Canadian dentists from July 29, 2020, through February 12, 2021, should be reassuring to the dental and general community. PRACTICAL IMPLICATIONS: Although the infection rates were low among Canadian dentists, it is important to continue to collect disease surveillance data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".