Teledentistry content in Canadian dental and dental hygiene curricula
Bibliographic record
Abstract
OBJECTIVES: To explore the extent to which teledentistry (TD) content is incorporated into Canadian dental and dental hygiene curricula. METHODS: An anonymous survey was distributed among all 10 dental and 35 dental hygiene programs across Canada in June and July 2022. The survey focused on TD teaching (methods employed, content taught, and barriers to TD education), with descriptive (frequency, maximum, minimum, mean) and inferential (Pearson chi-square for odds ratio test) data analyses using SPSS. A ρ - value < 0.05 was considered statistically significant. RESULTS: Thirty-four programs responsed to the survey, including all dental (n = 10) and 68% (n = 24) of dental hygiene programs; eighteen reported having TD content, including three dental programs. An average of 9.22 ± 4.86 h was reported for teaching TD, with lecture format as the most employed approach and using TD in dental practice as the most covered topic. While 53% of the dental hygiene programs employed formative and summative assessments, only one dental program reported having assessment for this content. Moreover, programs that dedicated less than 9 h to teaching this content were less likely to address more than seven topics (Odds ratio (OR) = 0.14). CONCLUSION: The dental and dental hygiene programs in Canada differ in their offering TD education, and 30% of the dental and 62.5% of the surveyed dental hygiene programs addressed TD content. This scarcity emphasizes the necessity for incorporating such content in future curriculum planning to consequently decrease the lack of knowledge, an identified barrier to TD implementation in education and practice, as today's students will be future educators.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".