Implementation Strategies to Enhance Teledentistry Adoption in Dental Care Settings: A Realist Review Protocol
Bibliographic record
Abstract
Introduction: Teledentistry has been suggested as an innovative and promising approach, in addition to face-to-face intervention, to improve the access and the continuity of oral health care. Despite the growing evidence on teledentistry, it is still unclear under what context, circumstances, and for whom it works best, as well as how and why its adoption succeeds or fails in different dental care settings. Objective: To synthesize evidence regarding: i) strategies to enhance teledentistry implementation; ii) interactions between the context-mechanisms-outcomes (CMOs) to maximize its integration in dental care settings. Methods: A realist review will be conducted following Pawson and Tilley’s framework. Searches were conducted across five databases such as MEDLINE (Covidence), Embase, CINAHL, PsycINFO, Cochrane Library, and grey literature. The review will include all teledentistry modalities, settings, and populations, with no restrictions on language or publication date. Two independent reviewers will iteratively select and screen studies, and extract data from included studies. Data synthesis will involve a narrative summary, along with content and descriptive analyses. Implementation strategies will be categorized using the Expert Recommendations for Implementing Change framework, and outcomes will be mapped with Proctor’s Implementation outcomes Framework. Conclusion: The results will suggest a better description of the implementation strategies to improve teledentistry implementation. By identifying contexts, mechanisms, and outcomes we will inform policy-makers, researchers, oral health care providers, and patients on what works, for whom, under what conditions and how, for its successful implementation. These findings may contribute to the evidence-based teledentistry practices, inform education, and support policy development.
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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 | 0.104 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
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".