How can clinical practice guidelines be adapted to facilitate shared decision making? A qualitative key-informant study
Bibliographic record
Abstract
BACKGROUND: To explore how clinical practice guidelines can be adapted to facilitate shared decision making. METHODS: This was a qualitative key-informant study with group discussions and semi-structured interviews. First, 75 experts in guideline development or shared decision making participated in group discussions at two international conferences. Next, health professionals known as experts in depression or breast cancer, experts on clinical practice guidelines and/or shared decision making, and patient representatives were interviewed (N=20). Using illustrative treatment decisions on depression or breast cancer, we asked the interviewees to indicate as specifically as they could how guidelines could be used to facilitate shared decision making. RESULTS: Interviewees suggested some generic strategies, namely to include a separate chapter on the importance of shared decision making, to use language that encourages patient involvement, and to develop patient versions of guidelines. Recommendation-specific strategies, related to specific decision points in the guideline, were also suggested: These include structuring the presentation of healthcare options to increase professionals' option awareness; structuring the deliberation process between professionals and patients; and providing relevant patient support tools embedded at important decision points in the guideline. CONCLUSIONS: This study resulted in an overview of strategies to adapt clinical practice guidelines to facilitate shared decision making. Some strategies seemed more contentious than others. Future research should assess the feasibility and impact of these strategies to make clinical practice guidelines more conducive to facilitate shared decision making.
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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 | 0.098 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".