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Record W2165245719 · doi:10.1089/jwh.2007.0751

Heart Disease Prevention Practices among Immigrant Vietnamese Women

2008· article· en· W2165245719 on OpenAlexaff
Gloria D. Coronado, Erica Woodall, Hoai Do, Lin Li, Yutaka Yasui, Vicky Taylor

Bibliographic record

VenueJournal of Women s Health · 2008
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsUniversity of Alberta
FundersNational Center for Chronic Disease Prevention and Health PromotionNational Cancer InstituteCenters for Disease Control and Prevention
KeywordsVietnameseMedicineImmigrationDiseaseGerontologyDemographyDisease preventionEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cardiovascular disease is a leading cause of death in the United States as well as in many countries around the world, including Vietnam. METHODS: Using data from a household survey of Vietnamese American women aged 20-79 years in Seattle, Washington, collected in 2006 and 2007, we examined heart disease prevention practices. Multivariable analyses were conducted to examine the relationship between demographic factors and preventive behaviors. RESULTS: A total of 1523 immigrant women completed interviews. The average daily consumption of fruits and vegetables was 3.5 servings, and 31% of our sample reported being physically active (engaging in at least 30 minutes of physical activity 5 or more days per week). Few respondents reported being current smokers (1.5%). Over three quarters of women had received a recent blood pressure check and a recent cholesterol check. Age and length of time in the United States were strongly associated with several cardiovascular prevention behaviors. CONCLUSIONS: Our findings confirm the need for continued efforts to develop and implement targeted educational campaigns to reduce the risk of cardiovascular disease among Vietnamese American women.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.367
Teacher spread0.329 · 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

Citations12
Published2008
Admission routes1
Has abstractyes

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