How Often Are Dental Care Workers Exposed to Occupational Characteristics that Put Them at Higher Risk of Exposure and Transmission of COVID-19? A Comparative Analysis.
Bibliographic record
Abstract
INTRODUCTION: Occupational characteristics of dental care - including closed environment, proximity to staff and patients and the use of aerosol-generating procedures - put workers at high risk of COVID-19 exposure and transmission. We describe the frequency of workplace situations that potentially increase the risk of exposure to COVID-19 in dental care compared with other occupations including health care. METHODS: We conducted a cross-sectional study using sociodemographic and occupational data from the 2016 Canadian census linked to workplace characteristics from the Occupational Information Network (O*NET) dataset. We assessed frequency of workplace indicators using an intensity score from 0 (low) to 100 (high) from O*NET on exposure to infection or disease, physical proximity to others, indoor controlled environments, standard protective equipment and specialized protective equipment. RESULTS: In 2016, 87 815 Canadians worked in the 5 dentistry occupations of interest: dentists; denturists; dental hygienists and dental therapists; dental technologists, technicians and laboratory assistants; and dental assistants. These occupations were routinely ranked in the top 10 of all occupations examined in terms of exposure to workplace indicators that increase the risk of exposure to COVID-19. Dental hygienists and dental therapists, dental assistants, dentists and denturists, rank as the top 4 occupations, in that order, with the highest exposure to disease or infection and physical proximity to others combined. CONCLUSIONS: Compared with other occupations, dental care workers are at a higher risk of occupational exposure to COVID-19. These results support the development of workplace guidance to reduce the risk of COVID-19 transmission and enhance the well-being of the dental care workforce.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".