Barriers and Enablers to Implementing Teledentistry From the Perspective of Dental Health Care Professionals: Protocol for a Systematic Quantitative, Qualitative, and Mixed Studies Review (Preprint)
Bibliographic record
Abstract
BACKGROUND There is growing literature on the potential of digital technologies for improving access to, ensuring continuity and quality of health care, and to strengthen health systems. Some studies have reported the cost-effectiveness of teledentistry, its reliability for remote dental screening, diagnosis, consultation, and treatment planning. Nonetheless, current evidence suggests that teledentistry implementation faces many challenges and is not yet adopted by dental health care providers (DHCPs). Developing strategies to improve teledentistry adoption requires an understanding of the factors that promote or hinder its successful implementation. OBJECTIVE This systematic review aims to identify and synthetize barriers and enablers to implementing teledentistry as perceived by DHCPs in their clinical practices, using the Theoretical Domains Framework (TDF) and the Capacity, Opportunity, and Motivation Behavior (COM-B) model. METHODS This protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Protocols) checklist. Literature will be searched in the following databases: PubMed, Cochrane Library, Web of Science, CINAHL, Embase, and PsycINFO. We will perform additional searches on Google, Google Scholar, and ProQuest Dissertations & Theses Global, screen the references of the included studies to capture additional relevant studies, and contact the authors of studies if we need more details. We will consider studies using qualitative, quantitative, and mixed methods. There will be no restrictions on the publication date and dental setting. We will include studies published in French, English, and Portuguese. Two independent reviewers will select the study, extract data, and assess methodological quality using the Mixed Methods Appraisal Tool’s checklist. Data analysis will include a descriptive and a thematic content analysis. We will synthetize and categorize the barriers and enablers using the TDF and COM-B model and present a narrative synthesis of our results using tables, figures, and quotes. RESULTS By March 2023, the literature search has retrieved 7355 publications. We will identify the range of barriers and enablers to implementing teledentistry through DHCPs’ perspectives. Considering the critical need for theory-based implementation interventions to improve the use of evidence-informed practices, we will synthesize the factors influencing the adoption of teledentistry based on the TDF domains and the 3 essential conditions predicting behavior change in accordance with the COM-B model. As needed, we will include additional determinants if not included in the TDF. We will conduct some subgroups analyses if studies are sufficient. We expect to complete the review by July 2024. CONCLUSIONS This review will provide some insights on the determinants of teledentistry implementation as perceived by DHCPs in dental settings. These findings will cater to patients, families, DHCPs, researchers, academic and professional decision-makers, and policy makers. The results of the systematic review could be used to develop theory-led interventions in improving teledentistry implementation. CLINICALTRIAL PROSPERO CRD42021293376; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=293376 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/44218
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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 | 0.089 | 0.107 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.091 | 0.010 |
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".