Unlocking Patterns of Alcohol Consumption in British Columbia Using Alcohol Sales Data: A Foundation for Public Health Monitoring
Bibliographic record
Abstract
Alcohol sales data provide a more accurate indication of alcohol consumption than alternative methods such as population surveys. This information can be used to better understand epidemiological issues related to alcohol consumption, policy development and evaluation. Official sales records were collected for the 28 regional districts of British Columbia (BC) for 2002–2005, while homemade alcohol was estimated from survey data. Alcohol consumption rates were found to vary across geographic regions, by season, and with population level demographics. Government stores were the largest source of alcohol consumption in BC, accounting for 45.1% of total alcohol consumption in 2004. U-Brews/U-Vins accounted for 4.0%, private liquor stores accounted for 27.5% of the total, and homemade alcohol made up 4.3% of total alcohol consumption. Analysis also revealed that the average alcohol concentration in wines (12.53%) and coolers (6.77%) has been underestimated by Statistics Canada. The feasibility of developing this type of alcohol monitoring system is examined. Finally, implications for the development of targeted public health initiatives and future research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".