Health systems strengthening to arrest the global disability burden: empirical development of prioritised components for a global strategy for improving musculoskeletal health
Bibliographic record
Abstract
INTRODUCTION: Despite the profound burden of disease, a strategic global response to optimise musculoskeletal (MSK) health and guide national-level health systems strengthening priorities remains absent. Auspiced by the Global Alliance for Musculoskeletal Health (G-MUSC), we aimed to empirically derive requisite priorities and components of a strategic response to guide global and national-level action on MSK health. METHODS: Design: mixed-methods, three-phase design.Phase 1: qualitative study with international key informants (KIs), including patient representatives and people with lived experience. KIs characterised the contemporary landscape for MSK health and priorities for a global strategic response.Phase 2: scoping review of national health policies to identify contemporary MSK policy trends and foci.Phase 3: informed by phases 1-2, was a global eDelphi where multisectoral panellists rated and iterated a framework of priorities and detailed components/actions. RESULTS: Phase 1: 31 KIs representing 25 organisations were sampled from 20 countries (40% low and middle income (LMIC)). Inductively derived themes were used to construct a logic model to underpin latter phases, consisting of five guiding principles, eight strategic priority areas and seven accelerators for action.Phase 2: of the 165 documents identified, 41 (24.8%) from 22 countries (88% high-income countries) and 2 regions met the inclusion criteria. Eight overarching policy themes, supported by 47 subthemes, were derived, aligning closely with the logic model.Phase 3: 674 panellists from 72 countries (46% LMICs) participated in round 1 and 439 (65%) in round 2 of the eDelphi. Fifty-nine components were retained with 10 (17%) identified as essential for health systems. 97.6% and 94.8% agreed or strongly agreed the framework was valuable and credible, respectively, for health systems strengthening. CONCLUSION: An empirically derived framework, co-designed and strongly supported by multisectoral stakeholders, can now be used as a blueprint for global and country-level responses to improve MSK health and prioritise system strengthening initiatives.
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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 | 0.096 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".