Interprofessional education about shared decision making for patients in primary care settings
Bibliographic record
Abstract
With an increasingly complex array of interventions facing healthcare professionals and patients, coupled with a potentially diverse number of professionals operating within the primary care team, the adoption of shared decision making (SDM) - with or without patients' decision aids - in an interprofessional manner is essential to ensure the highest quality of care for patients. In this article, we propose a framework for interprofessional education about SDM targeted to primary care settings. Five areas of knowledge and skills were agreed to be essential for all relevant stakeholders for interprofessional education in SDM to be successful: understanding the concept of SDM; acquiring relevant communication skills to facilitate SDM; understanding interprofessional sensitivities; understanding the roles of different professions within the relevant primary care group; and acquiring relevant skills to implement SDM. We suggest a series of teaching methods for the aforementioned areas, using principles from adult learning.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".