Person-centered care for musculoskeletal pain: Putting principles into practice
Bibliographic record
Abstract
Person-centered care specifically focuses on the whole person and is an important component of contemporary care for people with musculoskeletal pain conditions. Evidence suggests however, that some clinicians experience difficulties with integrating person-centered care principles into their clinical practice. Therefore, the purpose of this masterclass is to provide a framework that enables clinicians to incorporate person-centered principles in their management of people with musculoskeletal pain conditions. To support clinicians in overcoming some of the reported obstacles, we provide practical recommendations aimed at putting principles of person-centered care into practice. The framework supporting clinicians' delivery of person-centered care in practice consists of three key-principles: A) a biopsychosocial understanding of the person's experience; B) person-focused communication; and C) supported self-management. The framework includes three phases: 1) identification and goal setting, 2) coaching to self-management, and 3) evaluation. Building a therapeutic relationship underpins these phases and is an overarching element that weaves through the key-principles and phases of the framework. We use a clinical case to illustrate the practical implementation of these recommendations.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".