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Record W2087852643 · doi:10.1016/s1474-5151(09)60108-0

SMMP6 Cardiovascular Risk Modification in Two Regions of Ontario, Canada: How Does Place Matter?

2009· article· en· W2087852643 on OpenAlexaffabout
Jan Angus, Ellen Rukholm, Isabelle Michel, Lisa Seto, Jennifer Lapum, Frate S. Del, Katherine E. Timmermans, Sylvie Larocque

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsToronto Metropolitan UniversityUniversity of SudburyLaurentian UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Purpose: Individuals at high risk for coronary heart disease (CHD) benefit from risk modification. However, many people struggle to change established lifestyle patterns. The purpose of this qualitative study was to examine constraints and supports in the maintenance of behaviour change. Methods: We used qualitative methods to examine health-related experiences of 38 individuals who had received information about cardiovascular risk modification. Men and women were recruited from two regions of Ontario, Canada: a large metropolitan centre in the south of the province (20 participants) and a small city in the northern region (12 Anglophone, 6 Francophone participants). Focus groups were followed by photo-elicitation, wherein participants photographed everyday scenes or objects that influenced their cardiovascular risk modification. Then, individual interviews focused on the contextual and personal meanings represented in the photographs. Data analysis for each subgroup was conducted by separate teams. Interviews with Francophones were conducted and analysed in French. The analysis focused on the places or physical locations of everyday life, as well as activities that made these places meaningful.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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 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

Citations0
Published2009
Admission routes2
Has abstractyes

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