Implementation of Motivational Interviewing Training in Dental Education and Research—A Scoping Review
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: Motivational interviewing (MI) is an evidence-based counseling approach that enhances behavior change by strengthening an individual's motivation. While MI research in dental education is growing, the scope of MI training programs has not been systematically examined. This review assesses the implementation of MI training in dentistry. METHODOLOGY: A scoping review followed the PRISMA-ScR checklist and JBI manual for evidence synthesis. A systematic search (May 2023) using MeSH terms, keywords, and operators was conducted in Medline (Ovid), Embase (Ovid), CINAHL, PsycINFO (Ovid), Web of Science, and ProQuest. Articles were screened via Covidence, and data were extracted based on variables informed by the DoCTRINE framework. RESULTS: Of 17 studies, 70.6% were US-based, and 64.7% were published after 2010. MI training targeted dental hygiene and dentistry students (47% each) through lectures, role-play, e-learning, and workshops. Evaluations used pre-post questionnaires, recorded interactions, and tools like MITI, MISC, and OSCE. Training duration varied from single sessions (11.7%) to longitudinal programs (82.3%), enhancing students' confidence, communication, and public health readiness. However, skill retention required ongoing reinforcement. Barriers included time constraints, patient resistance, and limited faculty support. High-quality reporting (DoCTRINE score) was observed in 47% of studies. CONCLUSION: MI training enhances dental students' communication and patient-centered care. Long-term skill retention requires continued practice, and hybrid instructional strategies can improve scalability. Faculty competency and structured longitudinal training are crucial for effective MI implementation.
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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 | 0.082 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".