The need for adaptable global guidance in health systems strengthening for musculoskeletal health: a qualitative study of international key informants
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal (MSK) conditions, MSK pain and MSK injury/trauma are the largest contributors to the global burden of disability, yet global guidance to arrest the rising disability burden is lacking. We aimed to explore contemporary context, challenges and opportunities at a global level and relevant to health systems strengthening for MSK health, as identified by international key informants (KIs) to inform a global MSK health strategic response. METHODS: An in-depth qualitative study was undertaken with international KIs, purposively sampled across high-income and low and middle-income countries (LMICs). KIs identified as representatives of peak global and international organisations (clinical/professional, advocacy, national government and the World Health Organization), thought leaders, and people with lived experience in advocacy roles. Verbatim transcripts of individual semi-structured interviews were analysed inductively using a grounded theory method. Data were organised into categories describing 1) contemporary context; 2) goals; 3) guiding principles; 4) accelerators for action; and 5) strategic priority areas (pillars), to build a data-driven logic model. Here, we report on categories 1-4 of the logic model. RESULTS: Thirty-one KIs from 20 countries (40% LMICs) affiliated with 25 organisations participated. Six themes described contemporary context (category 1): 1) MSK health is afforded relatively lower priority status compared with other health conditions and is poorly legitimised; 2) improving MSK health is more than just healthcare; 3) global guidance for country-level system strengthening is needed; 4) impact of COVID-19 on MSK health; 5) multiple inequities associated with MSK health; and 6) complexity in health service delivery for MSK health. Five guiding principles (category 3) focussed on adaptability; inclusiveness through co-design; prevention and reducing disability; a lifecourse approach; and equity and value-based care. Goals (category 2) and seven accelerators for action (category 4) were also derived. CONCLUSION: KIs strongly supported the creation of an adaptable global strategy to catalyse and steward country-level health systems strengthening responses for MSK health. The data-driven logic model provides a blueprint for global agencies and countries to initiate appropriate whole-of-health system reforms to improve population-level prevention and management of MSK health. Contextual considerations about MSK health and accelerators for action should be considered in reform activities.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".