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Cardiovascular Health Promotion in Aging Women: Validating a Population Health Approach

2005· article· en· W2043297564 on OpenAlexafffund
Jo‐Ann V. Sawatzky, Barbara J. Naimark

Bibliographic record

VenuePublic Health Nursing · 2005
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsUniversity of Manitoba
FundersResearch ManitobaManitoba Health Research Council
KeywordsHealth promotionCardiovascular healthContext (archaeology)OperationalizationPopulationGerontologyMedicinePopulation healthPublic healthDiseaseEnvironmental healthNursingPathologyGeography

Abstract

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OBJECTIVE: Although cardiovascular disease is the leading cause of death in North American women, most cardiovascular research has focused on men. In addition, while there has been a recent trend toward population health promotion (PHP) and a consequent focus on the broad determinants of health, there is still a dearth of research evidence related to the promotion of cardiovascular health within this context. The purpose of this study was to explore and describe the interrelationships between the determinants of health and individual cardiovascular health/risk behaviors in healthy women, within the context of a framework for PHP. DESIGN: A comprehensive inventory of factors affecting the cardiovascular health of women was operationalized in a survey questionnaire, the Cardiovascular Health Promotion Profile. Physical measures were also taken on each participant (n = 206). RESULTS: The multivariate analyses support significant interrelationships between the population health determinants and multiple individual cardiovascular health/risk behaviors in this cohort (p < 0.05). CONCLUSIONS: The evidence from this study provides foundational validation for a population health approach and population-based strategies for cardiovascular health promotion in women. Further research, within the context of a PHP framework, is central to building on the body of knowledge in this area.

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)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.347
Teacher spread0.289 · 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.

Study designOther design
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

Citations3
Published2005
Admission routes2
Has abstractyes

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