Training health professionals in shared decision making: Update of an international environmental scan
Bibliographic record
Abstract
Objective To update an environmental scan of training programs in SDM for health professionals. Methods We searched two systematic reviews for SDM training programs targeting health professionals produced from 2011 to 2015, and also in Google and social networks. With a standardized data extraction sheet, one reviewer extracted program characteristics. All completed extraction forms were validated by a second reviewer. Results We found 94 new eligible programs in four new countries and two new languages, for a total of 148 programs produced from 1996 to 2015—an increase of 174% in four years. The largest percentage appeared since 2012 (45.27%). Of the 94 newprograms, 42.55% targeted licensed health professionals (n = 40), 8.51% targeted pre-licensure (n = 8), 28.72% targeted both (n = 27), 20.21% did not specify (n = 19), and 5.32% targeted also patients (n = 5). Only 23.40% of the new programs were reported as evaluated, and 21.28% had published evaluations. Conclusions Production of SDM training programs is growing fast worldwide. Like the original scan, this update indicates that SDM training programs still vary widely. Most still focus on the single provider/patient dyad and few are evaluated. Practice implications This update highlights the need to adapt training programs to interprofessional practice and to evaluate them.
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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 | 0.113 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 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".