Implementation of Shared Decision-Making in the Management of Chronic Musculoskeletal Pain: <i>a scoping review</i>
Bibliographic record
Abstract
Abstract Shared decision-making (SDM) is increasingly recommended for managing chronic musculoskeletal pain, yet its use and implementation in clinical practice remains poorly understood. This scoping review aimed to identify and synthesize barriers and facilitators to the implementation and use of SDM across healthcare settings. A systematic search of seven databases conducted in April 2025 yielded 28 eligible studies. Using a deductive–inductive–deductive analysis approach, we mapped findings to existing SDM taxonomies and the Theoretical Domains Framework. We identified 16 themes and 46 subthemes spanning patient-, clinician-, interactional-, and system-level factors. Key facilitators included SDM training, decision aids, effective communication, empathetic care, trust, and strong therapeutic alliances. Barriers included time constraints, lack of individualized care, insufficient knowledge, conflicting beliefs about pain, and system-level obstacles such as limited resources or organizational support. Prominent theoretical domains included knowledge, skills, beliefs about consequences, and environmental context and resources. These findings offer a comprehensive overview of multilevel factors shaping SDM in musculoskeletal care. Future studies should focus on developing context-sensitive knowledge translation interventions to overcome barriers and leverage facilitators to promote the uptake of SDM in musculoskeletal pain. Trial registration: https://doi.org/10.17605/OSF.IO/SH8G4 .
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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 | 0.052 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".