Development, Evaluation, and Initial Findings of New York State Department of Health Community Drug Checking Pilot Programs
Bibliographic record
Abstract
CONTEXT: The illicit drug landscape in the United States is dynamic, featuring a risky and erratic drug supply. Drug checking programs (DCP) have been successfully implemented and studied extensively in Canada and Europe but are scarce in the United States. Integrating DCP at harm reduction programs provides an opportunity to engage people at the point-of-care and deliver a combination of harm reduction services, access to healthcare services, and linkages to treatment. PROGRAM: The New York State Department of Health (NYSDOH) developed and supports operation of 8 pilot community DCP sites throughout the state. The DCP were trained to utilize Fourier-transform infrared spectroscopy (FTIR) technology to deliver real-time results to participants. IMPLEMENTATION: The NYSDOH community DCP pilot began development in 2022. Partnerships were formed across multiple domains including other DCP, universities, forensic laboratories, syringe service and harm reduction programs, and legal and regulatory offices within the NYSDOH. The first pilot sites began operating in mid-2023 and program expansion is on-going. EVALUATION: Evaluation staff were extensively engaged in development and implementation phases. Qualitative evaluation focused on barriers, facilitators, and lessons learned from program staff and technicians. Quantitative evidence was gathered to assess the reach of the DCP and accuracy of results attained by drug checking technicians during their training periods. Drug checking results helped characterize the illicit drug supply. DISCUSSION: Development and implementation of DCP in NYS was facilitated by strong partnerships across sectors including public health and harm reduction. DCP may involve diverse partners who do not regularly collaborate, and health departments are positioned to build relationships and convene partners for program implementation. Evaluation findings highlight the importance of facilitating on-going training and technical assistance to DCP for quality assurance. The initial successes and lessons learned from the NYSDOH DCP demonstrate state public health departments' ability to successfully deploy this innovative harm reduction strategy.
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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 | 0.068 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".