PAUSE: The Development and Implementation of a Novel Brief Intervention Program Targeting Cannabis and Alcohol Use Among University Students
Bibliographic record
Abstract
ABSTRACT Objectives: Studies on brief interventions (BIs) for substance misuse among university students indicate their effectiveness for alcohol use, however, programs have rarely addressed cannabis use and comorbid mental health symptoms. This paper describes the development and implementation of a manualized single-session BI called PAUSE, based on motivational interviewing (MI) and harm reduction principles, designed to address alcohol and cannabis misuse and concurrent mental health concerns among university students on a mid-sized university campus. Methods: Participants were recruited by self-referral and professional referral. Interested participants completed an online assessment followed by a 45- to 90-minute in-person MI session with a trained counsellor. Results: A total of 46 (61% male) students attended a PAUSE session; 54.5% attended for cannabis. Fifty percent of individuals were referred from counsellors or physicians. Ninety-five percent of the samples were engaging in problematic cannabis use and/or moderate to high-risk alcohol use and 80% were identified as being at risk for an anxiety or depressive disorder. Patients and staff responded favorably to the intervention in narrative feedback. No adverse events occurred. Conclusions: PAUSE appears to be a feasible and promising BI to address postsecondary students at high risk for cannabis and alcohol use disorders, who frequently have concurrent mental health concerns. Further studies using randomized, controlled designs, and mixed methods with longitudinal follow-up are necessary. Objectifs: Les études sur les interventions brèves (BIs) pour la toxicomanie chez les étudiants universitaires indiquent leur efficacité pour la consommation d’alcool, mais les programmes ont rarement abordé la consommation de cannabis et les symptômes de santé mentale concomitante. Cet article décrit le développement et la mise en œuvre d’une BI à session unique appelée Pause, basée sur des principes d’entrevue motivationnelle (MI) et de réduction des risques, visant à lutter contre l’abus d’alcool et de cannabis et les problèmes de santé mentale concomitants chez les étudiants universitaires d’un campus universitaire de grosseur moyenne. Méthodes: Les participants ont été recrutés par auto-référence et référence professionnelle. Les participants intéressés ont complété une évaluation en ligne suivie d’une séance d’MI de 45 à 90 minutes en présence d’un conseiller qualifié. Résultats: 46 étudiants (61% d’hommes) ont assisté à une session PAUSE; 54,5% étaient présents pour le cannabis. 50% des personnes ont été référées par des conseillers ou des médecins. 95% de l’échantillon se livraient à une consommation problématique de cannabis et / ou à une consommation d’alcool modérée à élevée et 80% étaient identifiés comme étant à risque de développer un trouble anxieux ou dépressif. Les patients et le personnel ont répondu favorablement à l’intervention dans la rétroaction narrative. Aucun événement indésirable n’est survenu. Conclusions: La PAUSE semble être une intervention brève réalisable et prometteuse pour les étudiants de niveau poste-secondaire à risque élevé de troubles liés au cannabis et à l’alcool, qui ont souvent des problèmes de santé mentale concomitants. D’autres études utilisant des modèles contrôlés randomisés et des méthodes mixtes avec suivi longitudinal sont nécessaires.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".