Predictors of Unhealthy Alcohol Consumption Behavior in Canadian Men
Bibliographic record
Abstract
Aims Men are more likely to engage in alcohol consumption, which can have long-term consequences. The objective of our study was to sample Canadian men to determine predictors of hazardous alcohol hazardous as well as predictors for change. Methods Canadian men were surveyed investigating demographics, medical comorbidities, health behaviours, and willingness to change. Alcohol consumption was classified based on validated Audit-C scoring (>3 was positive for dependency or abuse). Stages of change were classified based on the transtheoretical model of change (pre-contemplation, contemplation, preparation, action, and maintenance). Multivariate regression was performed to determine demographic factors as predictors for consumption and change. Results After exclusions and sample stratification, 2000 participants were included. Participants were aged 19–94 (median 48, IQR 34–60). Approximately 773 (38.7%) screened positive based on Audit-C scores. On multivariate analysis, minority status, age, work, income, retirement, living situation, geographic location, and level of education were associated with hazardous drinking. Of those engaging in hazardous drinking, the majority were in pre-contemplation or contemplation 488 (63.1%). On multivariate analysis, various demographic factors were associated with the five stages of change. Conclusion Our study illustrates that approximately 40% of men screen positive for unhealthy drinking behaviour and associated demographic risk factors for those at highest risk. The majority are in the earliest stages in transtheoretical model for change (>60%), and there exist only a few associated demographic risk factors. This warrants awareness of this national problem, insight for patient education and targeted interventions to address hazardous behaviour and reduce morbidity and mortality.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".