Ready for SDM- evaluation of an interprofessional training module in shared decision making – A cluster randomized trial
Bibliographic record
Abstract
OBJECTIVE: Ready for SDM was developed in Norway as a comprehensive modularized curriculum for health care providers (HCP). The current study evaluated the efficacy of one of the modules, a 2-hour interprofessional SDM training designed to enhance SDM competencies. METHODS: A cluster randomized controlled trial was conducted with eight District Psychiatric Centres randomized to wait-list control (CG) or intervention group (IG). Participants and trainers were not blinded to their allocation. The IG received a 2-hour didactic and interactive training, using video examples. The primary outcome was the agreement between the participants' and an expert assessment of patient involvement in a video recorded consultation. The SDM-knowledge score was a secondary outcome. RESULTS: Compared to the CG (n = 65), the IG (n = 69) judged involvement behavior in a communication example more accurately (mean difference of weighted T, adjusted for age and gender:=-0.098, p = 0.028) and demonstrated better knowledge (mean difference=-0.58; p = 0.014). A sensitivity analysis entering a random effect for cluster turned out not significant. CONCLUSION: The interprofessional group training can improve HCPs' SDM-competencies. PRACTICE IMPLICATIONS: Addressing interprofessional teams using SDM communication training could supplement existing SDM training approaches. More research is needed to evaluate the training module's effects as a component of large-scale implementation of 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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".