Physiotherapists’ Experiences with and Perspectives on Implementing an Evidence-Based, Chronic Pain Self-Management Programme in Primary Health Care: A Qualitative Study
Bibliographic record
Abstract
Purpose: Evidence suggests that a physiotherapist-led chronic pain self-management programme in primary health care (PHC) improves function for people living with chronic pain; however, implementing a new approach to care can be difficult. In this study, we sought to understand the experiences of physiotherapists who had implemented the ChrOnic pain self-ManageMent support with pain science EducatioN and exerCisE (COMMENCE) programme; its perceived barriers, facilitators, benefits, and drawbacks; and how the physiotherapists tailored the programme to their own clinical contexts. Method: This interpretive description qualitative study used semi-structured interviews with physiotherapists who had implemented the COMMENCE programme in PHC. Results: Themes from 11 interviews included experiences of personal and professional growth, increasing confidence with experience, and changing the culture of pain management. Barriers and drawbacks to implementation included resource intensiveness, balancing programme demands with other clinical work, and challenges with patient attendance and participation. Facilitators included training, programme design and materials, supportive teams, and previous knowledge. Benefits included offering group and individualized support, evidence-based content, and sparking interest in learning more about pain management. The participants made small changes to tailor the programme content and delivery to their context. Conclusions: This study provides a rich understanding of the experiences, barriers, facilitators, benefits, drawbacks, and tailoring related to the COMMENCE programme in PHC. The results will facilitate future implementation of this intervention in PHC settings.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".