Exploring daily variations of drinking in the Swiss general population. A growth curve analysis
Bibliographic record
Abstract
This study aims to address the underlying trajectories of weekly individual drinking patterns by growth models and to relate differences in drinking patterns to socio-demographic and drinking characteristics of respondents. Data came from a two-stage stratified random subsample of 747 persons aged 15 years or more from a Swiss study on alcohol consumption using a within-subject design conducted between March 1999 and July 1999. Beverage specific assessment of daily alcohol consumption was obtained by a weekly drinking diary and other characteristics via telephone interviews. The diary had to be filled out on seven consecutive days. The growth models accounted for up to 37.6% of the initial error variance and provided evidence for two distinct, negatively correlated underlying trajectories of drinking patterns. The first trajectory described an increase in consumption from Monday to Sunday. The second trajectory was about a specific weekend consumption culminating on Saturday with a significantly higher growth rate among young people and heavy episodic drinkers than in other subgroups. Therefore, young and heavy episodic drinkers may be exposed to sudden adverse consequences of alcohol consumption during the weekend. Prevention efforts which are targeted to this subgroup should take its specific drinking pattern into account.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".