Theory-Informed Development of a Multicomponent Intervention to Implement Clinical Practice Guideline Recommendations in the Management of Shoulder Pain
Bibliographic record
Abstract
OBJECTIVE: Suboptimal primary health care management of shoulder pain has been reported in previous studies. Implementing clinical practice guidelines (CPGs) recommendations using a theoretical approach is recommended to improve shoulder pain management. This study aims to identify determinants of implementing recommendations from shoulder CPGs to help develop an intervention based on the identified determinants. METHODS: Family physicians and physical therapists managing patients with shoulder pain in primary care were invited to participate in a qualitative study to identify determinants to implementing recommendations from shoulder CPGs. The Theoretical Domains Framework (TDF) was used to inform the creation of the semi-structured interview guide and for deductive coding of transcriptions. The determinants were mapped to intervention functions and behavior change techniques (BCT) using the Behavior Change Wheel method and strategies for implementing CPGs recommendations were identified. RESULTS: Interviews were conducted with 16 family physicians and 19 physical therapists. We identified 12 barriers and 6 facilitators within 7 TDF domains: knowledge, skills, beliefs about capabilities, beliefs about consequences, intentions, environmental context and resources, and social influence. We identified 6 intervention functions and 12 BCT addressing the relevant determinants. The 11 implementation strategies identified include the development and distribution of educational material, interactive educational outreach visits, and audit and feedback. Other components to consider are the identification and preparation of champions in primary care clinical settings, revision of professional roles, and creation of interdisciplinary clinical teams. CONCLUSIONS: The identification of barriers and facilitators to implementing recommendations from shoulder CPGs allowed us to select implementation strategies at individual and organizational levels. IMPACT: The implementation strategies will be adapted to specific primary care contexts in consultation with stakeholders and operationalized into a multicomponent implementation intervention. Implementing the intervention has the potential to improve shoulder pain management in primary care and facilitate the use of evidence-based recommendations from CPGs.
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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 | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".