A distributed model to expand the reach of drug checking
Bibliographic record
Abstract
Purpose While there is increasing interest in implementing drug checking within overdose prevention, we must also consider how to scale-up these responses so that they have significant reach and impact for people navigating the unpredictable and increasingly complex drug supplies linked to overdose. The purpose of this paper is to present a distributed model of community drug checking that addresses multiple barriers to increasing the reach of drug checking as a response to the illicit drug overdose crisis. Design/methodology/approach A detailed description of the key components of a distributed model of community drug checking is provided. This includes an integrated software platform that links a multi-instrument, multi-site service design with online service options, a foundational database that provides storage and reporting functions and a community of practice to facilitate engagement and capacity building. Findings The distributed model diminishes the need for technicians at multiple sites while still providing point-of-care results with local harm reduction engagement and access to confirmatory testing online and in localized reporting. It also reduces the need for training in the technical components of drug checking (e.g. interpreting spectra) for harm reduction workers. Moreover, its real-time reporting capability keeps communities informed about the crisis. Sites are additionally supported by a community of practice. Originality/value This paper presents innovations in drug checking technologies and service design that attempt to overcome current financial and technical barriers towards scaling-up services to a more equitable and impactful level and effectively linking multiple urban and rural communities to report concentration levels for substances most linked to overdose.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".