Airborne Nicotine, Secondhand Smoke, and Precursors to Adolescent Smoking
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Secondhand smoke (SHS) directly increases exposure to airborne nicotine, tobacco’s main psychoactive substance. When exposed to SHS, nonsmokers inhale 60% to 80% of airborne nicotine, absorb concentrations similar to those absorbed by smokers, and display high levels of nicotine biomarkers. Social modeling, or observing other smokers, is a well-established predictor of smoking during adolescence. Observing smokers also leads to increased pharmacological exposure to airborne nicotine via SHS. The objective of this study is to investigate whether greater exposure to airborne nicotine via SHS increases the risk for smoking initiation precursors among never-smoking adolescents. METHODS: Secondary students (N = 406; never-smokers: n = 338, 53% girls, mean age = 12.9, SD = 0.4) participated in the AdoQuest II longitudinal cohort. They answered questionnaires about social exposure to smoking (parents, siblings, peers) and known smoking precursors (eg, expected benefits and/or costs, SHS aversion, smoking susceptibility, and nicotine dependence symptoms). Saliva and hair samples were collected to derive biomarkers of cotinine and nicotine. Adolescents wore a passive monitor for 1 week to measure airborne nicotine. RESULTS: Higher airborne nicotine was significantly associated with greater expected benefits (R2 = 0.024) and lower expected costs (R2 = 0.014). Higher social exposure was significantly associated with more temptation to try smoking (R2 = 0.025), lower aversion to SHS (R2 = 0.038), and greater smoking susceptibility (R2 = 0.071). Greater social exposure was significantly associated with more nicotine dependence symptoms; this relation worsened with higher nicotine exposure (cotinine R2 = 0.096; airborne nicotine R2 = 0.088). CONCLUSIONS: Airborne nicotine exposure via SHS is a plausible risk factor for smoking initiation during adolescence. Public health implications include limiting airborne nicotine through smoking bans in homes and cars, in addition to stringent restrictions for e-cigarettes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".