Advancing theories, models and measurement for an interprofessional approach to shared decision making in primary care: a study protocol
Bibliographic record
Abstract
BACKGROUND: Shared decision-making (SDM) is defined as a process by which a healthcare choice is made by practitioners together with the patient. Although many diagnostic and therapeutic processes in primary care integrate more than one type of health professional, most SDM conceptual models and theories appear to be limited to the patient-physician dyad. The objectives of this study are to develop a conceptual model and propose a set of measurement tools for enhancing an interprofessional approach to SDM in primary healthcare. METHODS/DESIGN: An inventory of SDM conceptual models, theories and measurement tools will be created. Models will be critically assessed and compared according to their strengths, limitations, acknowledgement of interprofessional roles in the process of SDM and relevance to primary care. Based on the theory analysis, a conceptual model and a set of measurements tools that could be used to enhance an interprofessional approach to SDM in primary healthcare will be proposed and pilot-tested with key stakeholders and primary healthcare teams. DISCUSSION: This study protocol is informative for researchers and clinicians interested in designing and/or conducting future studies and educating health professionals to improve how primary healthcare teams foster active participation of patients in making health decisions using a more coordinated approach.
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.
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 | 0.205 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".