Reviewing Teledentistry Usage in Canada during COVID-19 to Determine Possible Future Opportunities
Bibliographic record
Abstract
During the COVID-19 pandemic, the limited in-person availability of oral health care providers resulted in an unprecedented utilization of the teledentistry tool. This paper reviews how Canadian organizations supported teledentistry and what can be expected about its usage in the post-pandemic era. An environmental scan across relevant Canadian federal, provincial, and territorial organizations was conducted to review pertinent publicly available documents, including dental regulators' or associations' COVID-19 guidance documents, government documents, and media articles. Almost all jurisdictions promoted teledentistry for triaging dental emergencies and screening patients for COVID-19 symptoms but not even half of them have developed guidelines in terms of modalities of usage, handling of personal information, informed consent process, or maintaining standards of practice. During the COVID-19 recovery phase, these advances across Canada will support in developing a comprehensive guidance for teledentistry and possibly specific codes for its utilization. This can create a niche for teledentistry as an adjunct to the main stream dental care delivery where some visits can always be accommodated virtually, reducing disparities in oral healthcare between rural and urban communities. Ultimately, this can potentially make oral health care delivery more effective, efficient, and environmentally friendly in Canada.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".