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Record W4223443722 · doi:10.1186/s12961-022-00843-0

Understanding the sustainment of population health programmes from a whole-of-system approach

2022· article· en· W4223443722 on OpenAlexaff
Melanie Crane, Nicole Nathan, Heather McKay, Karen Lee, John Wiggers, Adrian Bauman

Bibliographic record

VenueHealth Research Policy and Systems · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British ColumbiaVancouver Coastal Health
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsPopulation healthPublic healthPopulationThematic analysisGovernment (linguistics)Health policyHealth services researchPublic relationsService delivery frameworkQualitative researchEnvironmental healthMedicinePolitical scienceBusinessNursingSociologyService (business)MarketingSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Population health prevention programmes are needed to reduce the prevalence of chronic diseases. Nevertheless, sustaining programmes at a population level is challenging. Population health is highly influenced by social, economic and political environments and is vulnerable to these system-level changes. The aim of this research was to examine the factors and mechanisms contributing to the sustainment of population prevention programmes taking a systems thinking approach. METHODS: We conducted a qualitative study through interviews with population health experts working within Australian government and non-government agencies experienced in sustaining public health programs at the local, state or national level (n = 13). We used a deductive thematic approach, grounded in systems thinking to analyse data. RESULTS: We identified four key barriers affecting program sustainment: 1) short term political and funding cycles; 2) competing interests; 3) silo thinking within health service delivery; and 4) the fit of a program to population needs. To overcome these barriers various approaches have centred on the importance of long-range planning and resourcing, flexible program design and management, leadership and partnerships, evidence generation, and system support structures. CONCLUSION: This study provides key insights for overcoming challenges to the sustainment of population health programmes amidst complex system-wide changes.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.021
Scholarly communication0.0130.013
Open science0.0030.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.913
GPT teacher head0.713
Teacher spread0.199 · 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

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Qualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2022
Admission routes1
Has abstractyes

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