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Record W2910630925 · doi:10.7895/ijadr.253

The GENAHTO Project (Gender and Alcohol’s Harm to Others): Design and Methods for a Multinational Study of Alcohol’s Harm to Persons Other than the Drinker

2018· article· en· W2910630925 on OpenAlexvenueno aff
Sharon C. Wilsnack, Thomas K. Greenfield, Kim Bloomfield

Bibliographic record

VenueThe International Journal of Alcohol and Drug Research · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsHarmMultinational corporationAlcoholPsychologySocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

AIMS: Most alcohol research has focused on how drinking harms the drinker. Research on alcohol's harms to others (AHTO) has studied primarily single or small groups of countries. This article describes the methodology of a new multinational study - GENAHTO - of how social and cultural contexts are related to AHTO, from the perspectives of both perpetrators and victims. DESIGN: of AHTO. The countries surveyed vary widely in alcohol policies, drinking cultures, gender-role definitions, and socioeconomic conditions. PARTICIPANTS: More than 140,000 men and women, aged 15-84, participated in the surveys. MEASURES: Individual-level measures include demographics, alcohol use patterns, and alcohol-related harms. Regional- and societal-level measures include socioeconomic conditions, drinking patterns, alcohol policies, gender inequality, and income inequality. FINDINGS: The project seeks to identify characteristics of AHTO victims and perpetrators; within-country regional differences in AHTO; and associations between national alcohol polices and individual and regional levels of AHTO. CONCLUSIONS: GENAHTO is the first project to assess AHTO in diverse societies. Its findings can inform policies to abate AHTO in varying cultural contexts.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.295
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.257
GPT teacher head0.520
Teacher spread0.263 · 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 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

Citations31
Published2018
Admission routes1
Has abstractyes

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