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Record W2730918267 · doi:10.1177/1049732317715246

Lifestyle Inequalities: Explaining Socioeconomic Differences in Preventive Practices of Clinically Overweight Women After Menopause

2017· article· en· W2730918267 on OpenAlexaffabout
Mélisa Audet, Alex Dumas, Rachelle Binette, Isabelle J. Dionne

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of OttawaUniversité de Sherbrooke
Fundersnot available
KeywordsSocioeconomic statusGerontologyOverweightMenopauseSociocultural evolutionVulnerability (computing)MedicineSocial classPsychologyEnvironmental healthObesitySociologyPopulation

Abstract

fetched live from OpenAlex

Excess weight and menopause are two major factors increasing aging women's vulnerability to chronic diseases. However, social position and socioeconomic status have also been identified as major determinants influencing both health behaviors and the development of such diseases. This study focuses on the socioeconomic variations of behavioral risk factors of chronic diseases in aging women. By drawing on Bourdieu's sociocultural theory of practice, 40 semistructured interviews were conducted to investigate preventive health practices of clinically overweight, postmenopausal women from contrasting socioeconomic classes living in Canada. Findings emphasize class-based differences with respect to long-term health and preventive practices according to three major themes: priority to long-term time horizons, attention given to risk factors of diseases, and control over future health. Health care providers should strive to work in concert with all subgroups of women to better understand their values, worldviews, and needs to decrease health inequalities after menopause.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
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 head0.534
GPT teacher head0.673
Teacher spread0.139 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2017
Admission routes2
Has abstractyes

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