Can shared decision making increase the uptake of evidence in clinical practice?
Bibliographic record
Abstract
Despite copious research and clear policies in many healthcare systems, evidence based practice has yet to be widely adopted. Part of the problem is insufficient consideration of the patient-clinician consultation, which lies at the heart of clinical practice and is where most decisions are made. Shared decision making (SDM)-the interactive process in which patients and clinicians decide on healthcare together-capitalises on the consultation to better translate the best evidence into clinical decisions while taking the patient's values and preferences into account. This paper takes stock of interventions that seek to embed SDM in clinical practice, such as patient decision aids that target both patients and clinicians. It also presents challenges that remain: among others, the paucity of evidence on effective implementation strategies and the lack of consideration of how SDM works when care is delivered by interprofessional teams. The paper then reviews current initiatives to improve and disseminate SDM across the healthcare continuum, and discusses why SDM should be encouraged as a means to leverage evidence based practice. The evidence suggests that finding ways to overcome the challenges and promote SDM will accelerate the uptake of evidence in gastroenterology and hepatology clinical practice.
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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 | 0.038 | 0.158 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.029 | 0.034 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".