Maternal cigarette smoking during pregnancy predicts drug use via externalizing behavior in two community‐based samples of adolescents
Bibliographic record
Abstract
Abstract Background and Aims Prenatal exposure to maternal cigarette smoking (PEMCS) is associated with a higher probability of substance use in adolescence. We explore if externalizing behavior mediates this relationship, while controlling for a number of potential covariates of this mediation process. Methods We used data obtained in two geographically distinct community samples of adolescents. The first (cross‐sectional) sample consisted of 996 adolescents (12–18 years of age) recruited from the Saguenay Youth Study (SYS) in Canada (47% with PEMCS). The second (longitudinal) sample consisted of 1141 adolescents (49% with PEMCS) from the Northern Finland Birth Cohort (NFBC1986). In both samples, externalizing behavior and substance use were assessed during adolescence. In the NFBC1986 cohort, externalizing behavior was also assessed in childhood. Results In both populations, PEMCS is associated with a higher likelihood of adolescent drug experimentation. In the NFBC1986 cohort, exposed (versus non‐exposed) adolescents experiment with an extra 1.27 [B = 0.24, 95% confidence intervals (CI) = 0.15, 0.33 P < 0.001] drugs. In the SYS cohort, a clear protective effect of not being exposed is shown: non‐exposed (versus exposed) adolescents are 1.5 times [B = −0.42, 95% CI = −0.75, −0.09, P = 0.013] less likely to take drugs. These associations between PEMCS and drug experimentation remain in the multivariate and mediational analyses. Conclusions Prenatal exposure to maternal cigarette smoking appears to be associated with a higher probability of experimenting with drugs during adolescence, both directly and indirectly via externalizing behavior and the number of peers reported as using drugs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".