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Record W2046873465 · doi:10.1108/14777270310471595

Cardiovascular health of immigrant women: implications for evidence-based practice

2003· article· en· W2046873465 on OpenAlexaffabout
Julia Wong, Shirley Wong

Bibliographic record

VenueClinical Governance An International Journal · 2003
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsDalhousie University
Fundersnot available
KeywordsImmigrationMedicineDemographyObesityDiseaseNational Health Interview SurveyPopulationGerontologyEnvironmental healthGeographyInternal medicine

Abstract

fetched live from OpenAlex

There is a dearth of information on the cardiovascular health of immigrant women. The present study analyzed the dataset from the National Population Health Survey to describe the modifiable CVD risk factors of Canadian immigrant women. Results indicated a statistically significant difference in modifiable CVD risk factors with respect to the country of birth, spoken language, and the length of time in Canada. The prevalence of smoking was significantly different across all income groups, with the lowest and middle income groups having the highest prevalence rate. Compared with their non-white counterparts, the white immigrant women had a greater prevalence of obesity, hypertension, and smoking. Irrespective of race and country of birth, immigrant women tended to have worse CVD risk factors than non-immigrant women. Age was the most important predictor of heart disease and hypertension. Implications of the study results for evidence-based practice are discussed.

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)0.062
metaresearch head score (Gemma)0.185
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.185
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0020.003
Scholarly communication0.0070.004
Open science0.0040.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.115
GPT teacher head0.484
Teacher spread0.369 · 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
Published2003
Admission routes2
Has abstractyes

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