Differential susceptibility to environmental influences: Interactions between child temperament and parenting in adolescent alcohol use
Bibliographic record
Abstract
Temperament and parental practices (PP) are important predictors of adolescent alcohol use (AU); however, less is known about how they combine to increase or decrease risk of AU. This study examined whether age 6 temperament (i.e., impulsivity and inhibitory control) interacted with age 6 coercive PP and/or age 14 parental monitoring to predict AU at 15 years among 209 adolescents. Results showed that low parental monitoring was associated with more frequent AU and that coercive PP interacted with impulsivity to predict AU. This interaction was examined as a function of two models that were not studied before in the prediction of AU: the diathesis-stress model (i.e., impulsive children are more "vulnerable" to adverse PP than those with an easy temperament); and the differential susceptibility model (i.e., impulsive children are also more likely to benefit from good PP). Results supported the differential susceptibility model by showing that impulsive children were not only at higher risk for AU when combined with high coercive PP but also benefit from the absence of coercive PP. This supports the suggestion that the conception of certain temperament characteristics, or in this case impulsivity, as a "vulnerability" for adolescent AU, may need revision because it misrepresents the malleability it may imply.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".