An implementation science study of an intersectoral (university – community – health authority) drug checking model
Bibliographic record
Abstract
Background. In British Columbia (BC), Canada, unregulated drug poisoning is the leading cause of death for people aged 10 to 59 years. While drug checking is an effective harm reduction intervention for people who use drugs (PWUD), the expense of associated technologies and the under-resourcing of harm reduction organizations hinders the implementation of these services. Globally, universities are increasingly assisting with the implementation and delivery of drug checking services, but little is known about the uptake, operations, and impacts of these partnered services. This study focuses on one such service delivered on a university campus and in the surrounding city of Kelowna, BC. Methods. This study used critical social theory, harm reduction principles, integrated knowledge translation, and the RE-AIM implementation science framework to meet the study’s objective of assessing the reach, effectiveness, adoption, implementation, and maintenance of the university-partnered service. Data were collected from June to December 2023 using one-on-one semi-structured interviews, online surveys, and arts-based data with decision makers, drug checkers, and students and community members who used drugs. Results. Participants (n=26) included seven decision makers, six drug checkers, four students, and nine community members. Service reach was enhanced through word-of-mouth promotion but inhibited by fears of criminalization and stigma as well as locations and hours. The service effectively reduced stigma and the risk of harm for service users and their social networks, as well as improved community service users’ quality of life. Decision makers and drug checkers adopted the service to reduce drug-related harms and for the complimentary nature of the intersectoral partnerships but were challenged by a lack of policies and support tools. Participants valued how services were implemented and emphasized the importance of peer collaborations. Decision makers attempted to maintain the service through improved accessibility and memorandums of understanding. Discussion. This study contributes valuable knowledge regarding drug checking in the university context and the role of universities in delivering community drug checking services. These contributions are discussed in the context of extant drug checking literature and demonstrate how universities might help to overcome challenges in the implementation and delivery of drug checking services for diverse populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".