Contemporary biopsychosocial exercise prescription for chronic low back pain: questioning core stability programs and considering context.
Bibliographic record
Abstract
This commentary explores the importance of considering the biopsychosocial model and contextual factors when prescribing exercise. Diverse exercise programs for patients with chronic low back pain (CLBP) produce similar outcomes, without one specific exercise protocol demonstrating clear superiority. One clear barrier to positive outcomes is poor exercise adherence. We suggest that there are certain common contextual factors present in all exercise prescription scenarios that may impact adherence and health-related outcomes. While challenging common core stability exercise prescription, we present an argument for enhancing and intentionally shaping the following contextual factors: the therapeutic alliance, patient education, expectations and attributions of therapeutic success or failure, and mastery or cognitive control over a problem. Overall, this commentary argues that to improve exercise adherence and outcomes in the CLBP population, the context in which exercise is delivered and the meaning patients embody need to be considered and shaped by clinicians.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.019 | 0.021 |
| 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".