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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".