The biopsychosocial model is lost in translation: from misrepresentation to an enactive modernization
Bibliographic record
Abstract
INTRODUCTION: There are increasing recommendations to use the biopsychosocial model (BPSM) as a guide for musculoskeletal research and practice. However, there is a wide range of interpretations and applications of the model, many of which deviate from George Engel's original BPSM. These deviations have led to confusion and suboptimal patient care. OBJECTIVES: 1) To review Engel's original work; 2) outline prominent BPSM interpretations and misapplications in research and practice; and 3) present an "enactive" modernization of the BPSM. METHODS: Critical narrative review in the context of musculoskeletal pain. RESULTS: The BPSM has been biomedicalized, fragmented, and used in reductionist ways. Two useful versions of the BPSM have been running mostly in parallel, rarely converging. The first version is a "humanistic" interpretation based on person- and relationship-centredness. The second version is a "causation" interpretation focused on multifactorial contributors to illness and health. Recently, authors have argued that a modern enactive approach to the BPSM can accommodate both interpretations. CONCLUSION: The BPSM is often conceptualized in narrow ways and only partially implemented in clinical care. We outline how an "enactive-BPS approach" to musculoskeletal care aligns with Engel's vision yet addresses theoretical limitations and may mitigate misapplications.
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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 | 0.033 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".