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Measuring alcohol consumption—should the ‘graduated frequency’ approach become the norm in survey research?

2005· article· en· W2065135361 on OpenAlexaff
Gerhard Gmel, Kathryn Graham, Hervé Kuendig, Sandra Kuntsche

Bibliographic record

VenueAddiction · 2005
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental HealthWestern University
FundersNational Institute on Alcohol Abuse and AlcoholismWorld Health Organization
KeywordsAlcohol consumptionNorm (philosophy)Consumption (sociology)AlcoholYield (engineering)Measure (data warehouse)EconometricsMathematicsStatisticsPsychologyMedicineEnvironmental healthComputer sciencePolitical scienceSociologyBiologySocial scienceData miningPhysics

Abstract

fetched live from OpenAlex

AIMS: To analyse whether recommendations for the graduated frequency (GF) approach to measure alcohol consumption are justified in a multi-cultural comparative study. DESIGN: Representative surveys, conducted between 1995 and 2003, of 10 countries participating in the GENACIS project (Gender, Alcohol and Culture: an International Study). MEASUREMENTS: Usual quantity, usual frequency and mean consumption per day measured with three instruments: GF, generic quantity-frequency (QF) and beverage-specific quantity-frequency (QFBS). FINDINGS: The GF did not consistently yield higher volumes and quantities across all countries compared with the generic QF, while the QFBS resulted in higher quantities and higher volumes compared with the GF (in all but one country) and the QF. Frequencies were mostly higher on the GF compared with the QF and QFBS but there was also evidence of over-reporting of frequencies with the GF. Results for the GF suggested that it was implemented improperly in at least three of the 10 countries. CONCLUSION: The GF does not appear to be appropriate for cross-cultural research. It results in over-reporting of frequencies and appears to be too complex to be administered correctly in many countries. The best measure for these purposes appeared to be the QFBS particularly because it captures more effectively the variability of different alcoholic beverages with different ethanol contents and consumption with different vessel sizes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.358
GPT teacher head0.385
Teacher spread0.026 · 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

Citations70
Published2005
Admission routes1
Has abstractyes

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