Can cannabis use be prevented by targeting personality risk in schools? Twenty‐four‐month outcome of the adventure trial on cannabis use: a cluster‐randomized controlled trial
Bibliographic record
Abstract
AIMS: To examine the effectiveness of a personality-targeted intervention program (Adventure trial) delivered by trained teachers to high-risk (HR) high-school students on reducing marijuana use and frequency of use. DESIGN: A cluster-randomized controlled trial. SETTING: Secondary schools in London, UK. PARTICIPANTS: Twenty-one secondary schools were randomized to intervention (n = 12) or control (n = 9) conditions, encompassing a total of 1038 HR students in the ninth grade [mean (standard deviation) age = 13.7 (0.33) years]. INTERVENTIONS: Brief personality-targeted interventions to students with one of four HR profiles: anxiety sensitivity, hopelessness, impulsivity and sensation-seeking. PRIMARY OUTCOME: marijuana use. Secondary outcome: frequency of use. Assessed using the Reckless Behaviour Questionnaire at intervals of 6 months for 2 years. Personality risk was measured with the Substance Use Risk Profile Scale. FINDINGS: Logistic regression analysis revealed significant intervention effects on cannabis use rates at the 6-month follow-up in the intent-to-treat sample [odds ratio (OR) = 0.67, P = 0.05, 95% confidence interval (CI) = 0.45-1.0] and significant reductions in frequency of use at 12- and 18-month follow-up (β = -0.14, P ≤ 0.05, 95% CI = -0.6 to -0.01; β = -0.12, P ≤ 0.05, 95% CI = -0.54 to 0.0), but this was not supported in two-part latent growth models. Subgroup analyses (both logistic and two-part models) reveal that the sensation-seeking intervention delayed the onset of cannabis use among sensation seekers (OR = 0.25, β = -0.833, standard error = 0.342, P = 0.015). CONCLUSIONS: Personality-targeted interventions can be delivered effectively by trained school staff to delay marijuana use onset among a subset of high-risk teenagers: sensation-seekers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".