Working with People to Make Changes: A Behavioural Change Approach Used in Chronic Low Back Pain Rehabilitation
Bibliographic record
Abstract
PURPOSE: To describe the approach used by a physiotherapist who led a rehabilitation programme for injured members of the military with chronic low back pain designed to enhance self-efficacy and self-management skills. METHOD: This in-depth qualitative study used audio- and video-recorded data from interviews and field observations. Using an inductive analysis process, discussion of emerging themes led to a description of the physiotherapist's approach. RESULTS: The approach has three elements: developing a trusting relationship through building rapport, establishing a need in patients' minds to be actively engaged in their rehabilitation, and finding workable rehabilitation solutions that are most likely to be adopted by individual patients. This approach fits into current theories about health behaviour change (e.g., Transtheoretical Model of Change, Motivational Interviewing, Motivational Model of Patient Self-Management and Patient Self-Management) and elements of the therapeutic alliance. Using the therapeutic alliance (rapport) and behaviour change techniques, the physiotherapist focused on the perceived importance of a behaviour change (need) and then shifted to the patient's self-efficacy in the solutions phase. CONCLUSIONS: If we recognize that rehabilitation requires patients to adopt new behaviours, becoming aware of psychological techniques that enhance behaviour change could improve treatment outcomes.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".