Implementation of musculoskeletal Models of Care in primary care settings: Theory, practice, evaluation and outcomes for musculoskeletal health in high-income economies
Bibliographic record
Abstract
Musculoskeletal conditions represent one of the largest causes of years lived with disability in high-income economies. These conditions are predominantly managed in primary care settings, and yet, there is a paucity of evidence on which approaches work well in increasing the uptake of best practice and in closing the evidence-to-practice gap. Increasingly, musculoskeletal models of service delivery (as components of models of care) such as integrated care, stratified care and therapist-led care have been tested in primary health care pathways for joint pain in older adults, for low back pain and for arthritis. In this chapter, we discuss why implementation of these models is important for primary care and how models are implemented using three case examples: we review implementation theory, principles and outcomes; we consider the role of health economic evaluation; and we propose key evidence gaps in this field. We propose the following research priorities for this area: investigating the generalisability of models of care across, for example, urban and rural settings, and for different musculoskeletal conditions; increasing support for self-management; understanding the importance of context in choosing a model of care; detailing how implementation has been undertaken; and evaluation of implementation and its impact.
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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 | 0.055 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".