Alcohol Mixed with Caffeinated Energy Drinks: Consumption Patterns and Trends Among Canadian Youth & Young Adults
Bibliographic record
Abstract
Use of caffeinated energy drinks (CEDs) and alcohol mixed with energy drinks (AmEDs) is a growing trend worldwide, and in Canada, youth and young adults are the biggest consumers. Health Canada has recently changed regulations for CEDs, mildly affecting AmEDs. There are growing concerns around AmED use, including adverse health effects, excessive caffeine and alcohol consumption, and risk behaviours. There is currently insufficient evidence around AmED use in Canada to adequately inform policy and support stricter regulations. The current study sought to examine AmED use among youth and young adults in Canada, including associations with socio-demographics and behavioural characteristics. Responses were collected from an online-survey for 1989 respondents in Canada between the ages of 12-24. AmED outcomes, including awareness, use, type of AmED, location of use, reasons for use and risk behaviour were examined with multivariate logistic regression models including covariates sex, age, ethnicity, BMI, province, sleep patterns, school grades, maternal education, spending money, sensation seeking and binge drinking. Approximately 25% of the total sample reported AmED use in their lifetimes, and 74% of users reporting use in the past 12 months. Ever having AmED was greater (at p<0.05) among older youth and young adults, those living in BC or AB, SK, MB, and who binge drink. Current AmED use was greater among non-‘Whites’, those who did not report sleep time, and who reported greater binge drinking. Binge drinking was associated with the majority of AmED outcomes examined, including ever use, current use, pre-mixed AmEDs, AmED served by a bartender, used for intoxication or energy, and AmED awareness. Consumption of AmEDs is common among youth and young adults, and strongly associated with age, binge drinking and location of residence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".