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Record W2401204688

Cardiovascular health in Canadian women: the bigger picture revisited.

2005· article· en· W2401204688 on OpenAlexaffabout
Jo‐Ann V. Sawatzky

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCardiovascular healthPopulation healthPopulationDiseasePerspective (graphical)MedicineStroke (engine)Health promotionEpidemiologyState (computer science)Foundation (evidence)Economic growthPublic healthGerontologyPolitical sciencePublic relationsEnvironmental healthNursingEconomicsPathologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is the leading cause of death in Canadian women. Recent projections suggest that the number of cardiovascular-related deaths among women will continue to increase for at least another decade (Heart & Stroke Foundation of Canada, 2003). Nurses are in pivotal roles to facilitate the development of strategies to promote cardiovascular health and prevent CVD in this population. These strategies must move beyond the current focus on the individual, to encompass the bigger picture of population health promotion. This paper revisits the current state of knowledge of the population-based determinants of cardiovascular health in women, incorporates a Canadian perspective by including relevant epidemiological data, and recommends strategies that extend beyond the individual to the broader community, policy, health services and research domains.

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)0.006
metaresearch head score (Gemma)0.012
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 score0.089
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.014
Science and technology studies0.0070.007
Scholarly communication0.0090.007
Open science0.0020.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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

Citations1
Published2005
Admission routes2
Has abstractyes

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