'I didn't pay her to teach me how to fix my back': a focused ethnographic study exploring chiropractors' and chiropractic patients' experiences and beliefs regarding exercise adherence.
Bibliographic record
Abstract
AIM: To inform future research and exercise prescription for patients with chronic low back pain (CLBP), this study explored chiropractors' and chiropractic patients' experiences and beliefs regarding the barriers and facilitators to prescribed exercise adherence. METHODS: A focused ethnographic approach was used involving 16 semi-structured interviews, including pilot interviews (n = 4) followed by interviews with chiropractors (n = 6) and chiropractic patients with CLBP (n = 6). RESULTS: Barriers and facilitators to prescribed exercise adherence revolved around four themes: diagnostic and treatment beliefs motivating behavior, passive-active treatment balance, the therapeutic alliance and patient-centered care, and exercise delivery. CONCLUSION: Exercise adherence may be facilitated in patients with CLBP with simple exercise prescription changes made by chiropractors. However, changing chiropractors' and patients' diagnostic and treatment beliefs that are barriers to exercise adherence appears challenging. Training chiropractors in pain neuroscience education and the intentional use of behavior change techniques warrants future investigation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".