Evaluation of a Drug Checking Training Program for Frontline Harm Reduction Workers and Implications for Practice
Bibliographic record
Abstract
CONTEXT: Drug checking, defined as the use of instruments (e.g. spectrometers), test strips, and other technologies to provide information on drug composition for harm reduction purposes, has emerged as a promising intervention to reduce harms of illicit drugs linked to overdose deaths. While demonstrating potential, these interventions remain limited in reach amid questions of how to reach the full population of people who use drugs and are at risk of overdose, including those outside urban areas. In response to these limitations, Substance, a drug checking project based in Victoria, Canada, developed a Distributed Model of Drug Checking and a concomitant training program. PROGRAM: The Distributed Drug Checking Training program eliminates need for point-of-care spectrometry technicians, instead capacitating harm reduction workers to provide drug checking using software developed by the project, infrared spectrometers, and immunoassay test strips. The training includes 5 hours of group content that can be delivered virtually, and 2 hours of practice time per learner. IMPLEMENTATION: Training and data collection took place between May 2022 and March 2024 with learners from 6 locations across Vancouver Island, Canada. We offered 13 training sessions, with evaluation data collected from 54 learners. EVALUATION: The training was evaluated across Kirkpatrick's 4 levels of training evaluation. The training was highly acceptable to learners, attributable to intended changes in knowledge and skill related to drug checking, resulted in competence to deliver drug checking through the project's Distributed Model, and facilitated expansion of drug checking services to 6 geographically distant locations. DISCUSSION: After completing the 7-hour training program, harm reduction workers were able to deliver drug checking without need for on-site drug checking technicians. The short duration of the training and its demonstrated success with the Distributed Model of Drug Checking make this a promising approach for expanding the reach of drug checking services.
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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 | 0.038 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".