Coffee and cigarettes: Examining the association between caffeinated beverage consumption and smoking behaviour among youth in the COMPASS study
Bibliographic record
Abstract
In adults, coffee, sugar-sweetened beverage (SSB) and high energy drink consumption have been related to increases in risky behaviour, including smoking. However, these associations are not well understood during adolescence. The purpose of this study was to examine the association between beverage consumption and smoking behaviour among Canadian adolescents. Using data from the COMPASS study (2016-2017; n = 46,957), four models were developed to investigate whether beverage consumption explained variability in smoking behaviour in adolescence (age = 15.7 ± 1.2 yrs); 1) smoking status; 2) e-cigarette use status; 3) days smoking cigarettes per month; and 4) days using an e-cigarette per month. Models were adjusted for demographic factors. Logistic (models 1 and 2) and ordinal logistic (models 3 and 4) were used for analysis. An association between the frequency of SSBs, coffee/tea or high energy drinks consumption and smoking behaviour was identified in all models. Greater beverage consumption was associated with being a current smoker (OR = 2.46 (2.02, 2.99)), former smoker, (OR = 2.50 (1.53, 4.08)), and currently using an e-cigarette (OR = 4.66 (3.40, 6.40)). Higher beverage consumption was also associated with more days smoking/using an e-cigarette per month (OR = 2.67 (1.92, 3.70) and 3.45 (2.32, 5.12), respectively). High energy drink consumption on 4 or 5 days of the school week was the best predictor of smoking behaviour in all models. Given the health consequences of smoking and e-cigarette use and their association with SSB, high energy drinks and coffee consumption, policy initiatives to prevent smoking initiation and limit access to these beverages needs ongoing attention and implementation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".