An implementation science study of a campus-based drug checking service at a Canadian university
Bibliographic record
Abstract
OBJECTIVES: Research shows that in countries around the world, unregulated drug use and disorders are more prevalent amongst youth and young adults compared to general adult populations. Further, youth and young adults, including those in post-secondary settings, are increasingly experiencing harms from the global unregulated drug supply. Yet, population-specific harm reduction strategies are limited. This implementation science study explored the reach, effectiveness, adoption, implementation, and maintenance of a post-secondary campus-based drug checking service that was facilitated through intersectoral partnerships and run by students. METHODS: This study used critical social theory and integrated knowledge translation. Data were collected from June to December 2023 through interviews and surveys that utilized the RE-AIM implementation science framework. One-time, individual semi-structured interviews were conducted with students (n = 4) who used drugs and/or the campus drug checking service and with program decision makers (n = 7) while student drug checking technicians (n = 6) completed online surveys. Data were analyzed using constant comparison. RESULTS: Service reach was enhanced by a motivation to avoid consuming harmful substances but inhibited by stigma and fears of criminalization, particularly amidst uncertain academic repercussions. Participants reported that the presence and use of student-run drug checking services effectively reduced stigma on campus and the risk of harm for service users and their social networks. Adoption and implementation were facilitated by intersectoral partnerships and associated network expansion and resource sharing opportunities but challenged by inadequate infrastructure. The service was maintained through partnership agreements and individual commitments to engaging with drug checking services. CONCLUSION: This study contributes valuable insights regarding drug checking in post-secondary settings. These contributions are discussed in the context of young adult health and post-secondary environments, demonstrating how post-secondary institutions might help to overcome challenges in the implementation and delivery of campus drug checking services. The sustainability of such services requires supportive policies, enhanced accessibility, and related evaluations.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".