School Prevention Programs Work Differently for Kids with Different Risk Profiles
A universal school prevention program reduced cannabis use across all risk levels, but the absolute effect was largest for high-risk children, whose predicted cannabis use probability dropped from 33% to 26%.
Quick Facts
What This Study Found
Researchers used a randomized controlled prevention trial with 1,874 sixth-graders (average age 11.8) to predict how a universal prevention program would affect individual children based on their specific risk profiles.
Using eight risk and protective factors, they calculated personalized probabilities of cannabis use for each child with and without the intervention. Low-risk children had predicted probabilities of 4.3% with intervention versus 6.5% without. Moderate-risk children: 10.9% versus 15.3%. High-risk children: 25.5% versus 32.6%.
School grades, thoughts of hurting oneself, and rule-breaking behavior were the three factors that most strongly distinguished high-risk from low-risk children.
Key Numbers
1,874 students; 33-month follow-up; low-risk: 4.3% vs. 6.5%; moderate-risk: 10.9% vs. 15.3%; high-risk: 25.5% vs. 32.6%; significant differences in all risk groups
How They Did This
School-based randomized controlled trial over 33 months with 1,874 sixth-graders. Two-level random intercept logistic model for panel data. Eight risk/protective factors used to create individualized risk profiles and predict intervention effects.
Why This Research Matters
This study demonstrates that universal prevention programs can be effective across all risk levels, but their impact varies substantially by individual risk profile. This personalized approach could help target resources more efficiently.
The Bigger Picture
Moving from population-level to individual-level prediction of prevention effects represents an important advance. Understanding which children benefit most can inform resource allocation and program design decisions.
What This Study Doesn't Tell Us
Predicted probabilities are model estimates, not observed outcomes. The eight risk factors may not capture all relevant predictors. Czech Republic sample may not generalize to other cultural contexts. Long-term effects beyond 33 months unknown.
Questions This Raises
- ?Can these personalized risk models be used in real-time to customize prevention delivery?
- ?Would additional risk factors improve prediction accuracy?
- ?Do the prevention effects persist into adolescence and adulthood?
Trust & Context
- Key Stat:
- High-risk children: probability dropped from 33% to 26% with intervention
- Evidence Grade:
- Randomized controlled trial with individualized risk modeling, though effects are predicted rather than directly observed at the individual level.
- Study Age:
- Published in 2015. Personalized prevention approaches have continued to develop.
- Original Title:
- Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.
- Published In:
- Addictive behaviors, 50, 110-6 (2015)
- Authors:
- Miovský, Michal, Vonkova, Hana, Čablová, Lenka, Gabrhelík, Roman
- Database ID:
- RTHC-01019
Evidence Hierarchy
Participants are randomly assigned to treatment or placebo groups to test cause and effect.
What do these levels mean? →Frequently Asked Questions
What makes a child high-risk for cannabis use?
The three most important factors were poor school grades, thoughts of hurting oneself, and rule-breaking behavior. The model used eight total risk and protective factors to create individualized profiles.
Should prevention only target high-risk kids?
The study found significant effects across all risk levels. Universal programs benefit everyone, but the absolute reduction in cannabis use probability is largest for high-risk children, suggesting both universal and targeted approaches have value.
Read More on RethinkTHC
- 420-sober-survival-guide
- CBT-cannabis-recovery
- cannabis-relapse-cycle-pattern
- cold-turkey-vs-taper-quit-weed
- dating-sober-after-quitting-weed
- exercise-quitting-weed-anxiety-brain
- grieving-quitting-weed-loss
- help-someone-quit-weed
- how-to-quit-weed
- how-to-talk-to-teenager-about-weed
- journaling-weed-withdrawal
- kids-friends-smoke-weed-parent-guide
- marijuana-anonymous-SMART-recovery-compare
- meditation-mindfulness-weed-withdrawal
- parent-smokes-weed-kids-hypocrite
- partner-still-smokes-weed
- partner-still-smokes-weed-quitting
- pink-cloud-sobriety-cannabis
- quit-weed-cold-turkey
- quit-weed-or-cut-back-which-is-better
- quit-weed-regret-went-back
- quitting-weed-20s
- quitting-weed-30s
- quitting-weed-after-years
- quitting-weed-during-crisis-divorce-job-loss
- quitting-weed-exercise
- quitting-weed-grief-loss-coping
- quitting-weed-legal-state
- quitting-weed-parent
- quitting-weed-success-stories
- quitting-weed-teenager-young-adult
- quitting-weed-triggers-environment
- relapsed-smoking-weed-what-to-do
- relapsed-weed
- should-i-quit-weed
- sober-music-festival-concert-without-weed
- supplements-weed-withdrawal
- teenager-smoking-weed-parent-guide
- telling-friends-quitting-weed
- weed-relapse-prevention-plan
- weed-relapse-why-it-happens
- weed-ritual-replacement
- weed-ruined-relationships
- weed-social-media-triggers-quit
Cite This Study
https://rethinkthc.com/research/RTHC-01019APA
Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman. (2015). Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.. Addictive behaviors, 50, 110-6. https://doi.org/10.1016/j.addbeh.2015.06.013
MLA
Miovský, Michal, et al. "Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.." Addictive behaviors, 2015. https://doi.org/10.1016/j.addbeh.2015.06.013
RethinkTHC
RethinkTHC Research Database. "Cannabis use in children with individualized risk profiles: ..." RTHC-01019. Retrieved from https://rethinkthc.com/research/miovsky-2015-cannabis-use-in-children
Access the Original Study
Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.
This study breakdown was produced by the RethinkTHC research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.