Part-time work and adolescent heavy episodic drinking: the influence of family and community context.
Bibliographic record
Abstract
OBJECTIVE: Previous studies on part-time work and alcohol use suggest that teenagers who work longer hours drink more heavily. The purpose of this study was to investigate whether family- and community-level factors moderate the relationship between part-time work hours and heavy episodic drinking. METHOD: Data were drawn from the Canadian Community Health Survey, a cross-sectional study of a nationally representative sample of Canadians. The survey included 8,080 respondents 15-19 years of age who reported work hours and frequency of heavy episodic drinking over the past 12 months. These respondents were located in 136 counties or municipalities across Canada. RESULTS: On average, work hours were positively associated with the frequency of heavy drinking by teenagers in the past 12 months. At the community level, the proportion of teenagers in each community drinking any alcohol was independently and positively associated with respondents' frequency of heavy drinking. In terms of moderating effects, we found that the work hours-drinking association was weaker among youth from low socioeconomic status families. Examination of community-level factors indicated that longer work hours were more strongly associated with heavy episodic drinking in communities with high rates of teen alcohol abstinence. CONCLUSIONS: Although the cross-sectional data prohibit any firm conclusions on how family and community factors influence the work-alcohol use relationship, these data suggest that interventions to reduce heavy episodic drinking among teens should address the broader environmental as well as the individual determinants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".