Do changes in per capita consumption mirror changes in drinking patterns?
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to examine how well per capita alcohol consumption figures derived from beverage sales data relate to changes over time in survey-based measures of drinking patterns. It was expected that strong associations would be found among these various measures of consumption. METHOD: Data from 12 household surveys conducted in Ontario between 1977 and 1997 provided information on: percentages of drinkers; daily drinkers; those drinking five or more drinks at a sitting weekly; those reporting two or more alcohol-related harms; and average number of drinks per week. These variables were then correlated with per capita consumption. RESULTS: Significant correlations were found only between per capita consumption and percentage of daily drinkers, and between percentage of drinkers and average number of drinks per week. CONCLUSIONS: The relationship of per capita consumption to survey measures of drinking is weak. The absence of consistent associations over time between per capita consumption and survey measures may be attributable to the small number of available data points or to increases in unrecorded consumption. Further research is needed to verify and explain these results.
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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) | 0.001 | 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".