E-cigarette use and quality of life in young adults: a Canadian health measure survey study
Bibliographic record
Abstract
Background: Research suggests that vaping raises oxidative stress levels, is associated with increased disease risk, and has been implicated in poor mental health. Objective: To assess cross-sectional associations between quality of life (QOL) indicators and e-cigarette (EC) use in young Canadian adults. Methods: We used data from the Canadian Health Measures Surveys. We compared physical activity (daily steps), physiological measurements, self-perceived stress, mental health and QOL between EC users (ever) and non-users (never). Multivariable binary or ordinal logistic regressions were used to calculate odds ratios (OR) and 95% confidence intervals (CI). Results: Analyses included 905 participants (15-30 years) with 115 (12.7%) reporting EC use and 790 non-users. After adjusting for confounders, comparing to non-users, EC users had significantly higher odds of being physically active (OR=2.19, 95%CI:1.14-4.20) but also with self-reported extreme chronic stress (OR=2.68, 95%CI:1.45-4.92). Albeit statistically non-significant, EC users also had higher odds of poorer QOL (OR=1.12, 95%CI: 0.64-1.95), but lower odds of other health morbidities (including high blood pressure, blood sugar or lower level of high-density lipoprotein). No statistically significant interactions between EC use, cigarette smoking, weed consumption and health outcomes were observed. Conclusion: Our study found that EC use was independently and significantly associated with increased odds of chronic stress and an indication of poorer QOL. Ongoing surveillance of young EC users is important to measure the long-term impact of vaping on their physical, mental health and quality of life and target for interventions.
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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 | 0.001 | 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".