Ready for shared decision making: Pretesting a training module for health professionals on sharing decisions with their patients
Bibliographic record
Abstract
INTRODUCTION: While shared decision-making (SDM) training programmes for health professionals have been developed in several countries, few have been evaluated. In Norway, a comprehensive curriculum, "klar for samvalg" (ready for SDM), for interprofessional health-care teams was created using generic didactic methods and guidance to tailor training to various contexts. The programmes adapted didactic methods from an evidence-based German training programmes (doktormitSDM). The overall aim was to evaluate two particular SDM modules on facilitating SDM implementation into clinical practice. METHOD: A descriptive mixed methods study using questionnaires and a focus group guided by the Medical Research Council Complex Interventions Framework. The training was provided as two different applications (module AB [introduction and SDM-basics] and module ABC [introduction, SDM-basics and interactive training]) with differing learning objectives, extent of interactivity, and duration (1 vs 2 hours). Groups of participants were recruited consecutively based on requests for health professional SDM training in university/college- and hospital-settings. By a focus group and a self-administered questionnaire comprehensibility, relevance and acceptance were assessed and qualitative feedback collected after the training. Data passed descriptive and content analysis, respectively. Knowledge was assessed twice using five multiple-choice items and analysed using paired t-tests. RESULTS: In 11 (six AB and five ABC) training sessions, 357/429 (296 AB and 133 ABC) eligible nurses, physicians and health professional students with varying clinical backgrounds and previous levels of SDM-knowledge participated. SDM-knowledge increased from 25-78% (range pretest) to 85-95% (range post-test) (P ≤ .001). The training was rated easy to understand, acceptable and relevant for practice. Findings to improve the education suggest higher emphasis on interprofessional teaching methods. CONCLUSIONS: The two SDM training modules met the basic requirements for use in a broader SDM implementation strategy and can even improve knowledge.
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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 | 0.020 | 0.358 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".