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Record W2964678279 · doi:10.23937/2469-5793/1510105

Critical Challenges of Economic and Social Issues in Secondary Prevention of Cardiovascular Disease

2019· article· en· W2964678279 on OpenAlexafffund
Rubina Barolia, Gina Higginbottom, Wendy Duggleby, Alexander M. Clark

Bibliographic record

VenueJournal of Family Medicine and Disease Prevention · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Realism in Sociology
Canadian institutionsUniversity of Alberta
FundersInternational Development Research Centre
KeywordsSocioeconomic statusPovertyDiseaseObesityMedicineEnvironmental healthHealthy eatingPsychologyGerontologyEconomic growthPhysical activityEconomicsPhysical therapyPathology

Abstract

fetched live from OpenAlex

What we eat may cause Cardiovascular Disease (CVD), and a healthy diet is a key factor in the prevention of CVD. Promoting healthy diet is challenging, particularly for people with low Socioeconomic Status (SES), because poverty is linked with many risk behaviours such as smoking, unhealthy eating, and obesity. Multiple factors make healthy eating very challenging. Underpinned by critical realism, this study explores the factors that inform Pakistani people of low socio-economic status SES in making decisions on food choices after diagnosis with CVD.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.065
GPT teacher head0.407
Teacher spread0.342 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2019
Admission routes2
Has abstractyes

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