Implementation strategies for integration of evidence-based caries management approach in dental education: A scoping review
Bibliographic record
Abstract
Despite the introduction of the Evidence-Based Caries Management (EBCM) approach over two decades ago, its integration into dental education and practice has been slow and inconsistent.For instance, a significant proportion of dental schools still adhere to outdated clinical practices, with surveys indicating only a minority of institutions have fully adopted EBCM principles.The lack of up-to-date dental education has been identified as a significant barrier to EBCM's implementation among dental practitioners.While there is existing literature related to the implementation of the EBCM approach in dental education, there is a lack of a comprehensive knowledge synthesis review on this topic.This scoping review aims to fill this gap by mapping and summarizing the evidence on implementation strategies of EBCM in dental education and identifying knowledge gaps. Objectives:This scoping review aims to map and summarize the evidence on implementation strategies of EBCM in dental education and to identify knowledge gaps. Methodology:Following the Joanna Briggs Institute manual and Arksey and O'Malley framework, an experienced librarian developed a comprehensive search strategy covering four databases, including MEDLINE (Ovid) and Scopus.Grey literature and hand searches through relevant journals and websites supplemented the retrieval.All study designs from 1990 to the present, excluding those conducted in private practices or focused on dental practitioners outside of educational settings, were included to focus on educational impacts.No language restrictions were applied.Two independent reviewers performed data screening, selection, and extraction. IIQualitative content analysis was adopted for data analysis, and Proctor's framework and ERIC taxonomy were used for data synthesis.Findings were reported following PRISMA-ScR guidelines, with most presented in tabular format. Results:Following the review of 1463 titles and abstracts and 50 full-text sources, 27 studies were included in this scoping review.These studies were published between 2007 and 2022 and were conducted at dental faculties in North and South America, Europe and Asia.Study designs included case studies, randomized control trials, non-randomized experimental studies, observational and mixedmethod studies.Key findings indicated that caries detection and risk assessment were the most prevalent components of EBCM integration.According to the ERIC taxonomy, twelve implementation strategies were identified, including 'local consensus discussions', 'continuous and dynamic training', 'informing local opinions', 'ongoing supervision', 'training the trainers', 'changing record systems'.Only 15 out of 27 included studies reported the implementation outcomes.These outcomes were related to the acceptance and adoptability of the EBCM implementation (e.g., consensus on cariology curricula; participants' enhancement in the knowledge, decision-making and performance; participants' satisfaction, perceptions, reaction, and readiness). Conclusions:This review indicates that the integration of the EBCM approach into undergraduate dental education is in its early stages.The studies reporting the implementation of the full EBCM approach are limited.Various strategies, including local consensus discussions, continuous and dynamic training, and continuous supervision have been used to implement the approach.Future studies should focus on evaluating implementation outcomes; including fidelity, patient-related III outcomes, and sustainability to better understand the impact of integrating the EBCM in dental education.
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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 | 0.100 | 0.217 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.041 | 0.029 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".