Youth Demographic Characteristics and Risk Perception of Using Alternative Tobacco Products: An Analysis of the 2014–2015 Canadian Student Tobacco, Alcohol, and Drugs Survey (CSTADS)
Bibliographic record
Abstract
BACKGROUND: There is a growing attraction by youth to alternative tobacco products (ATPs) such as e-cigarettes and hookahs. This study investigated risk perceptions and demographic characteristics associated with ATP use in grade 8-10 students. METHODS: Data were drawn from the 2014/15 cycle of the CSTADS. The analytic sample included 1819 students from a total pool of 42 094 students who completed the survey. Logistic regression models were used to examine factors (demographic characteristics and risk perception) associated with ATP use in the past 30 days. RESULTS: 12% of students in grade 8-10 self-identified as having used ATPs in the past 30-days, with a majority of students in grade 10 (56%). Male students had higher odds of reporting ATP use when compared to females. Although a lesser proportion of Indigenous students reported ATP use in comparison to White students (31% vs 61%), Indigenous students were 2.42 (1.49, 3.93) times as likely to use ATPs as White students. Students who perceived smoking hookah once in a while as "no to slight risk" were 1.58 (1.09, 2.28) times more likely to report ATP use than students who perceived "moderate to great risk." Also, students who perceived using e-cigarettes on a regular basis as "no to slight risk" were 2.21 (1.53, 3.21) times more likely to report ATP use as students who perceived "moderate-great risk." CONCLUSION: A significant number of grade 8-10 students use ATPs, especially e-cigarettes, with the misconception of minimal health risks. There remains the need to do more to counteract the rise in social and epidemiological alternative tobacco use trends among the youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".