A comparative multi‐level analysis of contextual drinking in American and Canadian adults
Bibliographic record
Abstract
AIM: To investigate the effects of demographic factors and drinking location on contextual drinking in a comparison of US and Canadian adults. DESIGN: Multi-level techniques were used to model the two-level hierarchical structure of drinking contexts (level 1) nested within individuals (level 2). PARTICIPANTS: Two random samples of current drinkers aged 18 years or older were drawn from Canada's Alcohol and Other Drugs Survey (CADS, 1994) and the 1995 National Alcohol Survey (NAS 9). The US sample included 2304 respondents (level 2) who reported a total of 5956 drinking contexts (level 1); in Canada, 5394 respondents reported 13 235 drinking contexts. MEASUREMENTS: Participants reported usual alcohol intake in up to four drinking locations. Demographic data included age, gender, education level, income and marital status. FINDINGS: Significant variation in usual alcohol intake was observed between drinking locations in both the US and Canada. The full multi-level models explained 25% of the variance at the contextual level and 25% and 22% at the individual level in the US and Canada. Contextual drinking was determined by a complex relationship between demographic characteristics and drinking locations. Some interactions between locations and demographic variables were observed for both the US and Canada, whereas others were observed only in the US sample. CONCLUSIONS: There is a need, from a population health perspective, for a multi-level approach in epidemiology and prevention that considers drinking setting as a relevant level for analysis and intervention.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".