Development of a multi-component intervention to improve the management of shoulder pain in primary care
Bibliographic record
Abstract
Context: Suboptimal primary care management of shoulder pain has been reported in several studies. Identifying barriers and facilitators to using recommendations from clinical practice guidelines (CPGs) is needed to ensure that knowledge translation interventions are tailored and promote better shoulder pain management. Objective: 1- To identify determinants (barriers and facilitators) to implementing recommendations from shoulder CPGs. 2- To map these determinants to implementation strategies for developing a multicomponent intervention to improve shoulder pain management in primary care. Study design and Analysis: Using a qualitative study design, we conducted semi-structured interviews that were recorded and transcribed into verbatims. Deductive thematic analysis based on the Theoretical Domains Framework (TDF) was performed. Using the Behaviour Change Wheel (BCW) method, determinants were mapped to Capability – Opportunity- Motivation – Behaviour components, intervention functions and behaviour change techniques (BCT). Based on this information, we identified relevant strategies to implement recommendations from CPGs. Setting: Several primary care settings in Quebec, Canada. Population studied: Family physicians and physiotherapists managing patients with shoulder pain. Intervention/Instrument: We developed a semi-structured interview guide informed by the TDF including questions related to determinants to implementing recommendations from shoulder CPGs. Results: Sixteen family physicians and 19 physiotherapists were interviewed. We identified 17 determinants to implementing shoulder CPGs’ recommendations across seven domains of the TDF (knowledge, skills, beliefs about capabilities, beliefs about consequences, intentions, environmental context and resources and social influence). We identified six interventions functions and 12 BCTs based on the determinants and TDF domains. Implementation strategies that were identified included the development and distribution of educational material, interactive workshops, support from clinical champions, audit and feedback, revision of professional roles and creation of interdisciplinary teams. Conclusion: We used a theory-based approach in the initial development of an intervention to implement shoulder CPGs recommendations in primary care. The intervention will be tailored to optimize its clinical implementation. This will likely result in better uptake by clinicians and more efficient shoulder pain management.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".