Core Competencies for Shared Decision Making Training Programs: Insights From an International, Interdisciplinary Working Group
Bibliographic record
Abstract
Shared decision making is now making inroads in health care professionals' continuing education curriculum, but there is no consensus on what core competencies are required by clinicians for effectively involving patients in health-related decisions. Ready-made programs for training clinicians in shared decision making are in high demand, but existing programs vary widely in their theoretical foundations, length, and content. An international, interdisciplinary group of 25 individuals met in 2012 to discuss theoretical approaches to making health-related decisions, compare notes on existing programs, take stock of stakeholders concerns, and deliberate on core competencies. This article summarizes the results of those discussions. Some participants believed that existing models already provide a sufficient conceptual basis for developing and implementing shared decision making competency-based training programs on a wide scale. Others argued that this would be premature as there is still no consensus on the definition of shared decision making or sufficient evidence to recommend specific competencies for implementing shared decision making. However, all participants agreed that there were 2 broad types of competencies that clinicians need for implementing shared decision making: relational competencies and risk communication competencies. Further multidisciplinary research could broaden and deepen our understanding of core competencies for shared decision making training.
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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 | 0.057 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".