Who under‐reports their alcohol consumption in telephone surveys and by how much? An application of the ‘yesterday method’ in a national <scp>C</scp>anadian substance use survey
Bibliographic record
Abstract
BACKGROUND AND AIMS: Adjustments for under-reporting in alcohol surveys have been used in epidemiological and policy studies which assume that all drinkers underestimate their consumption equally. This study aims to describe a method of estimating how under-reporting of alcohol consumption might vary by age, gender and consumption level. METHOD: The Canadian Alcohol and Drug Use Monitoring Survey (CADUMS) 2008-10 (n = 43 371) asks about beverage-specific 'yesterday' consumption (BSY) and quantity-frequency (QF). Observed drinking frequencies for different age and gender groups were calculated from BSY and used to correct values of F in QF. Beverage-specific correction factors for quantity (Q) were calculated by comparing consumption estimated from BSY with sales data. RESULTS: Drinking frequency was underestimated by males (Z = 24.62, P < 0.001) and females (Z = 17.46, P < 0.001) in the QF as assessed by comparing with frequency and quantity of yesterday drinking. Spirits consumption was underestimated by 65.94% compared with sales data, wine by 38.35% and beer by 49.02%. After adjusting Q and F values accordingly, regression analysis found alcohol consumption to be underestimated significantly more by younger drinkers (e.g. 82.9 ± 1.19% for underage drinkers versus 70.38 ± 1.54% for those 65+, P < 0.001) and by low-risk more than high-risk drinkers (76.25 ± 0.34% versus 49.22 ± 3.01%, P < 0.001). Under-reporting did not differ by gender. CONCLUSIONS: Alcohol consumption surveys can use the beverage-specific 'yesterday method' to correct for under-reporting of consumption among subgroups. Alcohol consumption among Canadians appears to be under-reported to an equal degree by men and women. Younger drinkers under-report alcohol consumption to a greater degree than do older drinkers, while low-risk drinkers underestimate more than do medium and high-risk drinkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".