Measuring alcohol consumption—should the ‘graduated frequency’ approach become the norm in survey research?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.510 | 0.646 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".