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Record W2077246046 · doi:10.2190/hs.43.3.h

Social Change and Women's Health

2013· article· en· W2077246046 on OpenAlexafffund
Peggy McDonough, Diana Worts, Anne McMunn, Amanda Sacker

Bibliographic record

VenueInternational Journal of Health Services · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
FundersEconomic and Social Research CouncilCanadian Institutes of Health Research
KeywordsAffect (linguistics)SituatedLife course approachSocial changePopulationSociologySocial determinants of healthDemographic economicsEconomic growthGender studiesPolitical sciencePsychologySocial psychologyEconomicsHealth careDemography

Abstract

fetched live from OpenAlex

Over the past five decades, the organization of women's lives has changed dramatically. Throughout the industrialized world, paid work and family biographies have been altered as the once-dominant role of homemaker has given way to the role of secondary, dual, or even primary wage-earner. The attendant changes represent a mix of gains and losses for women, in which not all women have benefited (or suffered) equally. But little is known about the health consequences. This article addresses that gap. It develops a "situated biographies" model to conceptualize how life course change may influence women's health. The model stresses the role of time, both as individual aging and as the anchoring of lives in particular historical periods. "Situating" biographies in this way highlights two key features of social change in women's lives: the ambiguous implications for the health of women as a group, and the probable connections to growing social and economic disparities in health among them. This approach lays the groundwork for more integrated and productive population-based research about how historical transformations may affect women's health.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.406
Teacher spread0.353 · 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.

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

Citations5
Published2013
Admission routes2
Has abstractyes

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