Determining Recommendations for Improvement of Communication Skills Training in Dental Education: A Scoping Review
Bibliographic record
Abstract
Professional communication skills have a significant impact on dental patient satisfaction and health outcomes. Communication skills training has been shown to improve the communication skills of dental students. Therefore, strengthening communication skills training in dental education shows promise for improving dental patient satisfaction and outcomes. The aim of this study was to facilitate the development of dental communication skills training through a scoping review with compilation of a list of considerations, design of an example curriculum, and consideration of barriers and facilitators to adoption of such training. A search to identify studies of communication skills training interventions and programs was conducted. Search queries were run in three databases using both text strings and controlled terms (MeSH), yielding 1,833 unique articles. Of these, 35 were full-text reviewed, and 17 were included in the final synthesis. Considerations presented in the articles were compiled into 15 considerations. These considerations were grouped into four themes: the value of communication skills training, the role of instructors, the importance of accounting for diversity, and the structure of communication skills training. An example curriculum reflective of these considerations is presented, and consideration of potential barriers and facilitators to implementation are discussed. Application and evaluation of these considerations are recommended in order to support and inform future communication skills training 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 | 0.051 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".