Enhancing interprofessionalism in shared decision-making training within homecare settings: a short report
Bibliographic record
Abstract
Training in shared decision-making (SDM) often focuses solely on dyadic relationships between one healthcare provider and one patient. However, many healthcare decisions often involve two or more health professionals. These decisions warrant utilizing an interprofessional shared decision-making (IP-SDM) approach which enables patients and their caregivers to face difficult decisions around care together. Most existing SDM training programs fall short when building interprofessional (IP) competencies and require an approach that integrates IP with SDM. This short report discusses the creation and trial implementation of three enhanced education tools (a video, role-play exercise with decision aid, and an IP observation aid) for an IP-SDM workshop focused on helping homecare teams collaborate with seniors and their caregivers throughout the decision-making process. We developed and implemented these tools in eight study sites of a larger randomized control trial to test the training workshop for homecare teams. The workshop and tools helped participants overcome interprofessional challenges in their work. Participants evaluated the tools and workshop, which offered guidance to better translate teachable IP collaboration competencies within SDM.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".