From Local Action to Global Policy: A Comparative Policy Content Analysis of National Policies to Address Musculoskeletal Health to Inform Global Policy Development
Bibliographic record
Abstract
BACKGROUND: Global policy to guide action on musculoskeletal (MSK) health is in a nascent phase. Lagging behind other non-communicable diseases (NCDs) there is currently little global policy to assist governments to develop national approaches to MSK health. Considering the importance of comparison and learning for global policy development, we aimed to perform a comparative analysis of national MSK policies to identify areas of innovation and draw common themes and principles that could guide MSK health policy. METHODS: framework adapted from the World Health Organization (WHO) Building Blocks and further inductive coding. Subsequently, texts were open coded and thematically analysed to derive specific sub-themes and principles underlying texts within each theme, serving as abstracted, transferable concepts for future global policy. RESULTS: The search yielded 165 documents with 41 retained after removal of duplicates and exclusions. Only three documents were comprehensive national strategies addressing MSK health. The most common conditions addressed in the documents were pain (non-cancer), low back pain, occupational health, inflammatory conditions, and osteoarthritis. Across eight categories, we derived 47 sub-themes with transferable principles that could guide global policy for: service delivery; workforce; medicines and technologies; financing; data and information systems; leadership and governance; citizens, consumers and communities; and research and innovation. CONCLUSION: There are few examples of national strategic policy to address MSK health; however, many countries are moving towards this by documenting the burden of disease and developing policies for MSK services. This review found a breadth of principles that can add to this existing work and may be adopted to develop comprehensive system-wide MSK health approaches at national and global levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".