Contexts, behavioural mechanisms and outcomes to optimise therapeutic exercise prescription for persistent low back pain: a realist review
Bibliographic record
Abstract
OBJECTIVE: Therapeutic exercises are a core treatment for low back pain (LBP), but it is uncertain how rehabilitative exercise facilitates change in outcomes. Realist reviews explore how the context (C) of certain settings or populations and underlying mechanisms (M) create intended or unintended outcomes (O). Our objective was to explore and understand the behavioural mechanisms by which therapeutic exercise creates change in outcomes of adherence, engagement and clinical outcomes for patients with LBP. METHODS: This was a realist review reported following the Realist and Meta-narrative Evidence Syntheses: Evolving Standards guidance. We developed initial programme theories, modified with input from a steering group (experts, n=5), stakeholder group (patients and clinicians, n=10) and a scoping search of the published literature (n=37). Subsequently, an information specialist designed and undertook an iterative search strategy, and we refined and tested CMO configurations. RESULTS: Of 522 initial papers identified, 75 papers were included to modify and test CMO configurations. We found that the patient-clinician therapeutic consultation builds a foundation of trust and was associated with improved adherence, engagement and clinical outcomes, and that individualised exercise prescription increases motivation to adhere to exercise and thus also impacts clinical outcomes. Provision of support such as timely follow-up and supervision can further facilitate motivation and confidence to improve adherence to therapeutic exercises for LBP. CONCLUSIONS: Engagement in and adherence to therapeutic exercises for LBP, as well as clinical outcomes, may be optimised using mechanisms of trust, motivation and confidence. These CMO configurations provide a deeper understanding of ways to optimise exercise prescription for patients with LBP.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".