Exploring approaches to teaching communication practices for shared decision-making in medical education: A scoping review
Bibliographic record
Abstract
Objective Health professional education researchers have documented increased training on shared decision-making (SDM) skills in undergraduate as well as continuing professional development (CPD). In this review, we seek to understand the current methods and approaches to teaching SDM for healthcare providers and trainees. Methods This scoping review involved the systematic search of 4 databases: MEDLINE, ERIC, PsycINFO, and Scopus for qualitative and quantitative studies literature covering the topic of teaching SDM in medical education. Results We identified 20 records that met our search criteria and were included in this study. We found that SDM training provided in medical education included a combination of teaching modalities: literature, didactics, simulations, role-playing games (RPGs), video tutorials, in-person feedback, case studies, pre-/post-assessments, online forum, group discussion, online modules, and decision boxes. We also found that the foci and results of the papers in the included studies could be classified into one of 5 main themes: prior knowledge of and training in SDM; impact on SDM skills of the participants; impact on the training on the confidence of the participants in practicing SDM; impact on knowledge of and attitudes towards SDM; and perceptions of the participants of the SDM training received. Conclusion Our scoping review shows that there is relatively limited literature available exploring teaching SDM in medical education. In addition, there is high heterogeneity in the documented approaches taken to teach SDM, to observe and measure the outcomes of the training, and to explore the factors supporting and impeding SDM training. Although there are different approaches to teaching SDM, serious gaming has been shown to have promising results as a training modality and warrants deeper investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".