Estimating how extra calories from alcohol consumption are likely an overlooked contributor to youth obesity
Bibliographic record
Abstract
INTRODUCTION: Youth obesity rates in Canada continue to rise. In this study, we produced conservative estimates of the potential excess calories from alcohol use across different alcohol consumption patterns common among Canadian youth to assess whether alcohol use should be considered in future obesity prevention strategies. METHODS: Using data from 10 144 Grade 12 students participating in the COMPASS study (2013/14), we estimated the number of calories consumed per year from alcohol consumption. Our estimates were based on three different generic types of alcoholic beverages, which were grouped according to average calorie content (vodka coolers; beer [5%]; and beer [4%], wine and liquor) across different frequencies of alcohol use and binge drinking. RESULTS: Results indicated high potential caloric intake for students who binge drank, as well as high variability in the estimates for calories consumed based on common consumption patterns for the different beverage types. For instance, 27.2% of students binge drank once per month, meaning they consumed between 6000 and 13 200 calories in one year (equivalent to 0.78 - 1.71 kg of fat). For the 4.9% of students who binge drank twice per week, the total calories in one year would range from 52 000 to 114 400 (equivalent to 6.74 - 14.83 kg of fat). CONCLUSION: Current recommendations for preventing youth obesity do not generally include any consideration of alcohol use. The high prevalence of frequent alcohol consumption and binge drinking by youth in this study and the substantial number of calories contained in alcoholic beverages suggest alcohol use among youth may warrant consideration in relation to youth obesity prevention.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".