Consumption of Caffeinated Energy Drinks Among Youth and Young Adults in Canada
Bibliographic record
Abstract
The growing market for caffeinated energy drinks (CEDs) has caused concern about excessive caffeine intake and potential adverse effects, particularly among young people. The current study examined patterns of CED consumption among youth and young adults in Canada, using data from a national online survey conducted in October 2014. Data from a non-probability sample of 2040 respondents aged 12-24 from a consumer panel was weighted to national proportions; measures of CED consumption were estimated, including prevalence, excessive daily consumption, and context for use (locations and reasons). Separate logistic regression models for two outcomes, past-week consumption and "ever" exceeding two energy drinks in a day (as per common guidance), were conducted to examine associations with demographic variables (sex, age, geographic region, race/ethnicity, and language). Overall, 73.6% of respondents reported "ever" consuming energy drinks; 15.6% had done so in the past week. Any consumption of energy drinks in the past week was more prevalent among males, Aboriginal respondents (vs. white only or mixed/other), and residents of British Columbia. Among "ever-consumers," 16.0% reported ever consuming more than two energy drinks in a day. Exceeding two in a day was more prevalent among older respondents (young adults aged 18-24), aboriginal respondents (vs. white only), and British Columbia residents. While the majority of youth and young adults had consumed energy drinks, about half were "experimental" consumers (i.e., consumed ≤ 5 drinks in their lifetime). Approximately one in six consumers had exceeded the usual guidance for maximum daily consumption, potentially increasing their risk of experiencing adverse effects.
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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 | 0.001 |
| 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 | 0.001 | 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".