Two Lifestyle Risks Intertwined: Parental Smoking Predicts Child Gambling Behavior at Age 12 Years
Bibliographic record
Abstract
BACKGROUND: Parental smoking can create a toxic environment for child development. A parental smoking lifestyle can predispose children to executive deficits, influencing precocious risk activities. Using a prospective birth cohort design, we examine the association between 2 lifestyle factors by estimating the relative contribution of long-term parental household smoking in predicting subsequent precocious child gambling behavior. METHOD: Parents reported on the amount of household smoke exposure from ages 1.5 to 7.5 years for children from the Quebec Longitudinal Study of Child Development. The main outcome measure was children's self-report of gambling behavior (at age 12 years). RESULTS: Sixty percent of parents reported that their children were never exposed to secondhand smoke in the home, while 27% and 13% reported transient and continuous levels of secondhand smoke, respectively. Overall, 16% of children reported gambling participation. When compared with never-exposed children, children exposed to secondhand smoke had 18% more chances of having participated in gambling at age 12 years (odds ratio = 1.18; 95% CI = 1.080-1.293). These results are adjusted for competing explanations and possible individual and family confounders. CONCLUSIONS: Higher levels of early childhood household smoke exposure are associated with greater odds of reporting gambling participation at age 12 years, which is more than several years before it is normative youthful behavior. By connecting the neurotoxic influence of one lifestyle factor on another, we show a nontrivial link between 2 public health issues (smoke exposure and precocious gambling) associated with considerable individual and societal costs that are amenable to community information campaigns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".