Deciphering programs for optimal self-management of persistent musculoskeletal-related pain and disability – Clinical implications for PTs
Bibliographic record
Abstract
The prolonged disability associated with musculoskeletal (MSK) pain represents an enormous health burden, for individuals as well as society. Promoting pain and disability management for patients with persistent MSK-related conditions can be very challenging for rehabilitation professionals. These often-complex conditions require the adoption of a biopsychosocial perspective in order to assess and address a vast array of potential factors affecting the patient. Fortunately, a self-management (SM) approach has been deemed effective in enhancing patients' control over their symptoms and disabilities. However, given the many different existing SM approaches, rehabilitation professionals would benefit from a clearer definition of SM and a better understanding of the basics of a SM program in order to facilitate their patients' development of SM skills, as this can lead to better outcomes. This narrative review explores the various components of an intervention program intended to facilitate patients' SM of their symptoms and disabilities resulting from a persistent MSK condition. It does so by drawing on a body of published work on pain and disability management, conceptual frameworks underlying SM programs, essential skills associated with optimal SM, and examples from the persistent low back pain (LBP) literature.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".