Interprofessionalism and shared decision-making in primary care: a stepwise approach towards a new model
Bibliographic record
Abstract
Most shared decision-making (SDM) models within healthcare have been limited to the patient-physician dyad. As a first step towards promoting an interprofessional approach to SDM in primary care, this article reports how an interprofessional and interdisciplinary group developed and achieved consensus on a new interprofessional SDM model. The key concepts within published reviews of SDM models and interprofessionalism were identified, analysed, and discussed by the group in order to reach consensus on the new interprofessional SDM (IP-SDM) model. The IP-SDM model comprises three levels: the individual (micro) level and two healthcare system (meso and macro) levels. At the individual level, the patient presents with a health condition that requires decision-making and follows a structured process to make an informed, value-based decision in concert with a team of healthcare professionals. The model acknowledges (at the meso level) the influence of individual team members' professional roles including the decision coach and organizational routines. At the macro level it acknowledges the influence of system level factors (i.e. health policies, professional organisations, and social context) on the meso and individual levels. Subsequently, the IP-SDM model will be validated with other stakeholders.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".