130 Ready for SDM – development of a modularized meta- curriculum for training healthcare professionals in shared decision-making
Bibliographic record
Abstract
Introduction The Norwegian healthcare system is committed to SDM and training healthcare professionals is crucial for successful implementation. Evaluated SDM training modules, including Germany’s doktormitSDM and trainings from the Ottawa Hospital Research Institute are available. In response to an identified lack of SDM training in Norway, we aimed to develop a meta-curriculum consisting of several evaluated evidence-based SDM training modules addressing various health care professionals. Methods Since implementation is an essential part of development, we chose The Knowledge-to- Action framework (KTA) as guidance for the research agenda. The first of the seven-step process involves identifying evidence-practice gaps, e.g.,inadequate SDM training. Each step may trigger new knowledge production to inform further implementation steps. Single components and entire modules of the meta-curriculum were evaluated using mixed and multiple study designs including piloting, pre-/post-test and RCT. Results The results align with the seven KTA-steps: 1) Assessment showed that SDM training is not implemented. 2) Adaptation of training modules involved translation adaption to context, formats, target groups, and timeframes. 3) Preliminary testing demonstrated the need for new training methods and modules due to identified barriers and enablers. 4) Tailored implementation strategies were chosen, such as utilizing certified SDM ambassadors trained in the Train-the-trainer-module. 5) Usage monitoring was carried out using process indicators like the number of conducted trainings. 6) Results from eight studies(N=937 participants) in tree health regions and multiple quality improvement projects(N=488 participants) demonstrated the positive reception, feasibility, and effective enhancement of SDM competencies through the Ready for SDM program. 7) For sustainability, a feedback-driven continuous learning system integrates certified trainers delivering SDM training and the meta-curriculum is incorporated into the strategy of the South-Eastern Health Authority. Discussion The meta-curriculum represents a novel approach, addressing barriers to SDM training implementation through its multi-modular, adaptive design, utilizing a generic pedagogical and interprofessional approach, and employing a continuous feedback-driven learning system, rather than a standardized curriculum. Conclusion Further evaluation is required to determine if ‘Ready-for-SDM’ as part of a multifaceted implementation strategy will improve the quality of healthcare decisions.
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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 | 0.040 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".