Management of Chronic Musculoskeletal Pain Through a Biopsychosocial Lens
Bibliographic record
Abstract
Chronic musculoskeletal pain continues to constitute a rising cost and burden on individuals and society on a global level, thus driving the demand for improved management strategies. The biopsychosocial model has long been a recommended approach to help manage chronic pain, with its consideration of the person and his or her experiences, psychosocial context, and societal considerations. However, the biomedical model continues to be the basis of athletic therapy and athletic training programs and therefore clinical practice. For more than 30 years, psychosocial factors have been identified in the literature as outcome predictors relating to chronic pain, including (but not limited to) catastrophizing, fear avoidance, and self-efficacy. Physical assessment strategies such as validated outcome measures can be used by the athletic therapist and athletic trainer to determine the presence or severity (or both) of nonbiogenic pain. Knowledge of these predictors and strategies allows the athletic therapist and athletic trainer to frame the use of exercise (eg, graded exposure), manual therapy, and therapeutic modalities in the appropriate way to improve clinical outcomes. Through changes in educational curricula content, such as those recommended by the International Association for the Study of Pain, athletic therapists and athletic trainers can develop profession-specific knowledge and skills that will enhance their clinical practice and enable them to better assist those living with chronic musculoskeletal pain conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".