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Record W2973217957 · doi:10.1136/bmjgh-2019-001806

Integrated prevention and management of non-communicable diseases, including musculoskeletal health: a systematic policy analysis among OECD countries

2019· article· en· W2973217957 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMJ Global Health · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of Ottawa
FundersNational Health and Medical Research CouncilMedical Research CouncilDepartment of Health, Government of Western AustraliaGovernment of Western AustraliaMitacsCurtin University of TechnologyWorld Health Organization
KeywordsNon-communicable diseaseEnvironmental healthHealth policyPublic healthMedicinePolitical scienceBusinessNursing

Abstract

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INTRODUCTION: Development and implementation of appropriate health policy is essential to address the rising global burden of non-communicable diseases (NCDs). The aim of this study was to evaluate existing health policies for integrated prevention/management of NCDs among Member States of the Organisation for Economic Co-operation and Development (OECD). We sought to describe policies' aims and strategies to achieve those aims, and evaluate extent of integration of musculoskeletal conditions as a leading cause of global morbidity. METHODS: Policies submitted by OECD Member States in response to a World Health Organization (WHO) NCD Capacity Survey were extracted from the WHO document clearing-house and analysed following a standard protocol. Policies were eligible for inclusion when they described an integrated approach to prevention/management of NCDs. Internal validity was evaluated using a standard instrument (sum score: 0-14; higher scores indicate better quality). Quantitative data were expressed as frequencies, while text data were content-analysed and meta-synthesised using standardised methods. RESULTS: After removal of duplicates and screening, 44 policies from 30 OECD Member States were included. Three key themes emerged to describe the general aims of included policies: system strengthening approaches; improved service delivery; and better population health. Whereas the policies of most countries covered cancer (83.3%), cardiovascular disease (76.6%), diabetes/endocrine disorders (76.6%), respiratory conditions (63.3%) and mental health conditions (63.3%), only half the countries included musculoskeletal health and pain (50.0%) as explicit foci. General strategies were outlined in 42 (95.5%) policies-all were relevant to musculoskeletal health in 12 policies, some relevant in 27 policies and none relevant in three policies. Three key themes described the strategies: general principles for people-centred NCD prevention/management; enhanced service delivery; and system strengthening approaches. Internal validity sum scores ranged from 0 to 13; mean: 7.6 (95% CI 6.5 to 8.7). CONCLUSION: Relative to other NCDs, musculoskeletal health did not feature as prominently, although many general prevention/management strategies were relevant to musculoskeletal health improvement.

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)0.003
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.024
GPT teacher head0.388
Teacher spread0.364 · 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