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Record W2384567340 · doi:10.1097/jom.0000000000000694

Cardiovascular Disease Risk Awareness and Its Association With Preventive Health Behaviors

2016· article· en· W2384567340 on OpenAlexaboutno aff
Josephine Jacobs, Shauna M. Burke, Michael Rouse, Sisira Sarma, Gregory S. Zaric

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthMedicineLogistic regressionDiseaseAssociation (psychology)Risk factorGerontologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to determine Canadian workers' level of awareness about their cardiovascular disease (CVD) risk factors and the association between CVD risk awareness and health behaviors. METHODS: We used cross-sectional data to compare awareness of CVD risk factors with biometric measures from a workplace screening clinic (n = 320). We assessed the association between risk factor awareness and self-reported health behaviors using logistic regression analyses. RESULTS: Overall, 39.5% of workers did not know at least one of their CVD risk factors. These individuals were less likely to meet recommended physical activity levels and to consume three daily servings of fruits and vegetables, and more likely to report weekly fast food consumption. CONCLUSIONS: This study highlights a lack of awareness about cholesterol levels and demonstrates a negative association between low CVD awareness and preventive health behaviors.

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 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.297
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations8
Published2016
Admission routes1
Has abstractyes

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