Teledentistry within oral health care providers' training: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Despite teledentistry (TD) ubiquitous utilization, it has yet to be fully adopted by oral health care providers' educational training. This study aimed to explore TD incorporation in the training of oral health care providers. METHODS: This review included studies on the TD content offered to oral health care providers. The JBI (Joanna Briggs Institute) methodology was used, and two reviewers screened the literature. Studies published between 1989 and 4 June 2022 were searched using "Teledentistry" and "Education" as initial keywords. The searched databases included MEDLINE, Cochrane Library, CINAHL, EPPI, Scopus, Epistemonikos, ERIC, MedEdPORTAL, ProQuest Dissertations and Theses Global, and Google Scholar. All of the relevant literature, regardless of their language, were added. The data were extracted using an extraction table and are presented in tabular and narrative summary formats. RESULTS: A total of 2180 documents were found, and 1804 documents were screened by the title and abstract after deduplication; 59 were selected for full-text review; and 19 were eligible for data extraction. Of all studies, 63.15% were published after the COVID-19 pandemic. Practicing TD and TD reimbursement were some of the addressed topics. Didactic education and hands-on practice were the most employed training methods. Self/peer evaluations and surveys were among the employed assessment techniques. CONCLUSION: TD education has been emphasized during the COVID-19 pandemic. There is a variation in the existing TD educational programmes, from addressing topics to assessment techniques. However, the number of educational programmes on this topic is sparse.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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 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".