Training health professionals in shared decision making: Update of an international environmental scan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To update an environmental scan of training programs in SDM for health professionals. 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. 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. 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. This update highlights the need to adapt training programs to interprofessional practice and to evaluate them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it