How does parental monitoring reduce adolescent substance use? Preliminary tests of two potential mechanisms
Bibliographic record
Abstract
Objective: To test two non-exclusive mechanisms by which parental monitoring might reduce teen substance use. The first mechanism is that monitoring increases punishment for substance use, since parents who monitor more are more likely to find out when substance use occurs (M1). The second mechanism is that monitoring directly prevents/averts teens from using substances in the first place for fear that parents would find out (M2).Method: 4,503 teens ages 11-15 years old in 21 communities across the U.S. (51% female, 9% Black, 17% Hispanic) completed a survey reporting on parents’ monitoring/knowledge and teen’s substance use.Results: We found no support for M1: Parents with greater parental monitoring were not more likely to be aware when the teen had used substances (odds ratios=0.79-0.93, ps=.34-.85), so they could not have increased the rate of punishment. We found support for M2: When asked directly, teens identified instances in which they planned to or had a chance to use substances but did not because their parents got in the way or would have found out (p<.01). Had all those opportunities of substance use occurred rather than been averted by parents, the rate of substance use in the sample would have been 1.4 times higher.Conclusion: In this community-based sample of teens, we failed to support prior punishment-centric theories of how monitoring might reduce teen substance use. Rather, monitoring may directly discourage teens from using substances regardless of whether it increases parents’ awareness of substance use or results in more punishment.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".