12-month follow-up of an exploratory ‘brief intervention’ for high-frequency cannabis users among Canadian university students
Bibliographic record
Abstract
BACKGROUND: One in three young people use cannabis in Canada. Cannabis use can be associated with a variety of health problems which occur primarily among intensive/frequent users. Availability and effectiveness of conventional treatment for cannabis use is limited. While Brief Interventions (BIs) have been shown to result in short-term reductions of cannabis use risks or problems, few studies have assessed their longer-term effects. The present study examined 12-month follow-up outcomes for BIs in a cohort of young Canadian high-frequency cannabis users where select short-term effects (3 months) had previously been assessed and demonstrated. FINDINGS: N=134 frequent cannabis users were recruited from among university students in Toronto, randomized to either an oral or a written cannabis BI, or corresponding health controls, and assessed in-person at baseline, 3-months, and 12-months. N=72 (54%) of the original sample were retained for follow-up analyses at 12-months where reductions in 'deep inhalation/breathholding' (Q=13.1; p< .05) and 'driving after cannabis use' (Q=9.3; p< .05) were observed in the experimental groups. Reductions for these indicators had been shown at 3-months in the experimental groups; these reductions were maintained over the year. Other indicators assessed remained overall stable in both experimental and control groups. CONCLUSIONS: The results confirm findings from select other studies indicating the potential for longer-term and sustained risk reduction effects of BIs for cannabis use. While further research is needed on the long-term effects of BIs, these may be a valuable - and efficient - intervention tool in a public health approach to high-risk cannabis use.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".