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Record W4293102416 · doi:10.1093/heapro/daac097

‘I have a healthy relationship with alcohol’: Australian midlife women, alcohol consumption and social class

2022· article· en· W4293102416 on OpenAlexaff
Belinda Lunnay, Kristen Foley, Samantha B. Meyer, Emma R. Miller, Megan Warin, Carlene Wilson, Ian Olver, Samantha Batchelor, Jessica A. Thomas, Paul Ward

Bibliographic record

VenueHealth Promotion International · 2022
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Waterloo
FundersAustralian Research CouncilAustralian Government
KeywordsAlcoholHealth promotionAlcohol consumptionPsychologyNarrativeSocial classPublic healthInjury preventionPoison controlGerontologySocial psychologyEnvironmental healthMedicinePolitical scienceNursing

Abstract

fetched live from OpenAlex

Alcohol consumption by Australian women during midlife has been increasing. Health promotion efforts to reduce alcohol consumption in order to reduce alcohol-related disease risk compete with the social contexts and value of alcohol in women's lives. This paper draws on 50 qualitative interviews with midlife women (45-64 years of age) from different social classes living in South Australia in order to gain an understanding of how and why women might justify their relationships with alcohol. Social class shaped and characterized the different types of relationships with alcohol available to women, structuring their logic for consuming alcohol and their ability to consider reducing (or 'breaking up with') alcohol. We identified more agentic relationships with alcohol in the narratives of affluent women. We identified a tendency for less control over alcohol-related decisions in the narratives of women with less privileged life chances, suggesting greater challenges in changing drinking patterns. If classed differences are not attended to in health promotion efforts, this might mitigate the effectiveness of alcohol risk messaging to women.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.148
GPT teacher head0.397
Teacher spread0.249 · 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

Citations40
Published2022
Admission routes1
Has abstractyes

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