Virtual Oral Health across Canada: A Critical Comparative Analysis of Clinical Practice Guidances during the COVID-19 Pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, teledentistry was suggested as a cost-effective and promising approach to improve access to oral health care. In response, Canadian provincial and territorial dental regulatory authorities (DRAs) published teledentistry-related clinical practice guidances (TCPGs). However, an in-depth comparison between them is needed to understand their gaps and commonalities so as to inform research, practice, and policy. This review aimed to provide a comprehensive analysis of TCPGs published by Canadian DRAs during the pandemic. A critical comparative analysis of these TCPGs published between March 2020 and September 2022 was conducted. Two members of the review team screened the official websites of dental regulatory authorities (DRAs) to identify TCPGs and performed data extraction. Among Canada's 13 provinces and territories, only four TCPGs were published during the relevant time period. There were some similarities and differences in these TCPGs, and we identified gaps pertaining to communication tools and platforms, and measures to safeguard patients' privacy and confidentiality. The insights from this critical comparative analysis and the unified workflow on teledentistry can aid DRAs in their development of new or an improvement to existing TCPGs or the development of nationwide TCP guidelines on teledentistry.
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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 | 0.031 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.031 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".