Infection Prevention Strategies Concordance in Canadian Dental Schools During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: The aim of this research was to identify variation in specific infection prevention and control (IPC) strategies across all dental schools in Canada and to evaluate the concordance concerning COVID-19 pandemic-related IPC strategies reported by clinic directors or IPC officers (CDs/IPCOs) and those reported by students, staff, and faculty in the schools. METHOD: A cross-sectional analysis within a prospective cohort study. Participants in the cohort study reported IPC strategies used in their schools during April or May 2021. Independently, CDs/IPCOs reported IPC strategies in school protocols in July 2021. RESULTS: Of the 600 participants recruited, 332 participants who were involved in the provision of in-person dental care were further analysed. Of the 16 IPC strategies investigated, only 3 were reported by CDs/IPCOs to be used at all schools, and another 8 strategies were used by 8 or 9 of 10 or by 1 of 10 schools, indicating that concordance across schools was good for 11 of 16 strategies. Agreement between study participants and the CDs/IPCOs varied considerably by strategy (ranging between 50% and 100%) and by school (ranging between 42.9% and 97.2%). The strategies with the highest mean agreement percentage across schools were "screening or interviewing patients before appointment for COVID-19-related symptoms" (92.7%) and "checking the temperature of the staff members at least once a day using a thermometer" (91.5%). CONCLUSIONS: The level of agreement in the use of strategies between participants working in clinics and CDs/IPCOs varied considerably by strategy and by school. Given the low COVID-19 infection rates in dental schools and the reported differences in IPC protocols, key strategies should be identified. During the pandemic, IPC protocols in Canadian dental schools evolved rapidly. Comparing different strategies might help develop a unified standard IPC protocol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".