Youth consumption of alcohol mixed with energy drinks in Canada: Assessing the role of energy drinks
Bibliographic record
Abstract
Consuming alcohol mixed with energy drinks (AmED) is a risk behaviour among youth, and previous research has reported a positive association between binge drinking and AmED consumption. However, limited research has examined how regular consumption of energy drinks is associated with AmED consumption among youth. The purpose of this report is to examine the role of energy drink use on AmED consumption in a Canadian youth population. Using data from the 2015-2016 COMPASS survey including 35,300 grade 9 to 12 students, two logistic regression models investigated if the inclusion of energy drink consumption in the past week altered the results of a model examining AmED consumption. In this sample, 13.2% of students reported AmED consumption in the last 12 months. Those who reported drinking energy drinks in the past week were 3.38 times more likely to consume AmED than those who did not drink energy drinks. The inclusion of past week energy drink use decreased the effect size of other associated substance use behaviours. This report demonstrates that past week energy drink use is associated with increased likelihood of AmED consumption and suggests that previous research may have missed this important contributor. These findings along with existing energy drink research highlight the importance of addressing the lack of energy drink regulations in Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".