Efficacy of a Web-Based Tailored Intervention to Reduce Cannabis Use Among Young People Attending Adult Education Centers in Quebec
Bibliographic record
Abstract
Background: Cannabis use is common among young adults. Web-based interventions are an increasingly popular way to reach this population. The aim of this study was to evaluate the efficacy of a Web-based tailored intervention, developed on theoretical and empirical grounds, to reduce cannabis use among young people by promoting a more positive intention to abstain. Methods: An experimental design was employed to evaluate the efficacy of the intervention in reducing cannabis use (primary outcome) by bolstering intention (secondary outcome) to abstain from use. Participants were randomly assigned either to an experimental group that received the Web-based tailored intervention or to a control group that did not. Results: Of 588 young adults (18–24 years of age) recruited, 295 were randomly assigned to the experimental group and 293 to the control group. At baseline, 343 reported using cannabis at least once in the past year. An intention-to-treat analysis showed that, at postintervention, a higher proportion of participants in the experimental group had reduced their cannabis use compared with the control group [10.8% vs. 5.1%, χ 2 (2) = 9.89, p = 0.007]. A mixed model for repeated measures revealed a statistically significant difference in terms of change in intention to abstain from cannabis use in the coming month [Group × Time interaction, F (1,474) = 8.03, p = 0.005]: intention increased for the experimental group (5.07 ± 2.07 to 5.45 ± 1.88; p < 0.001), but stayed stable for the control group (5.32 ± 2.03 to 5.36 ± 1.99; p = 0.779). Conclusion: This study shows that the intervention can be efficacious in reducing cannabis use among young people attending adult education centers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".