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Record W3176820494 · doi:10.1136/bmjgh-2021-006045

Health systems strengthening to arrest the global disability burden: empirical development of prioritised components for a global strategy for improving musculoskeletal health

2021· article· en· W3176820494 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMJ Global Health · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsSt. Michael's HospitalOntario Tech UniversityCanadian Chiropractic AssociationUniversity of TorontoCanadian Memorial Chiropractic College
FundersCurtin University of TechnologyUniversity of Sydney
KeywordsGlobal healthMedicineHealthcare systemPublic healthHealth careEconomic growthNursingEconomics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.438
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it