Survey Questions on Quantity and Frequency Are Differentially Effective by Age in Predicting Future Alcohol Consumption
Bibliographic record
Abstract
INTRODUCTION: Cross sectional research has demonstrated that screening tool questions on frequency of alcohol consumption are a better predictor of dependence and harmful drinking in younger adults; questions about quantity per occasion are a better predictor in older adults. The aim of this study is to see if this relationship also holds longitudinally. METHODS: A total of 9076 respondents aged 15 and over completed at least two waves of the longitudinal annual Household Income and Labour Dynamics in Australia survey 10 years apart between 2001-2010 and 2012-2020. Standardised scores from responses to questions on drinking quantity and frequency in the first survey were used to predict consumption 10 years later in groups stratified by age. RESULTS: Frequency of consumption was a significantly better predictor of future consumption than quantity in younger drinkers (aged < 36; β = 9.3, 95% confidence interval [CI] 8.6-10.0), than older drinkers (aged > 49; β = 5.1, 95% CI 4.8-5.5) while quantity was a better predictor in older drinkers (β = 8.2, 95% CI 7.2-9.3) than younger drinkers (β = 3.4, 95% CI 3.1-3.7). DISCUSSION AND CONCLUSIONS: Some commonly used screening items, such as drinking quantity and frequency, are differentially effective at identifying future heavy drinkers between age groups. Development of age-specific screening tools could potentially lead to more accurate identification of people who could benefit from intervention to reduce their alcohol consumption.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".