The Evaluation of a Drug Checking Software Platform that Enables Remote Point-of-Care Drug Checking
Bibliographic record
Abstract
In April 2016, drug-related overdoses were declared a public health emergency in British Columbia, Canada. At the heart of this public health emergency is fentanyl, a synthetic opioid and the most commonly detected drug in illicit drug toxicity deaths. However, the illicit drug supply as a whole has become increasingly unpredictable, especially since the COVID-19 pandemic disrupted British Columbia’s drug supply, leading to complex drug samples containing benzodiazepines and nitazenes, overdose on which is not reversed by naloxone, the opioid overdose reversal drug as they are not opioids. One harm reduction response to the overdose crisis is drug checking, a process in which a sample of an illicit drug is analyzed to determine its chemical composition. However, access to drug checking is not universal, and the implementation of drug checking services is hindered by several barriers, such as the need for skilled technicians to analyze drug checking data. In this thesis, I describe research I conducted to evaluate a drug checking software platform that facilitates the distributed drug checking model, a model by which drug checking is performed without skilled technicians being geographically present. The research conducted in this thesis comprises two studies: a heuristic evaluation of the software and semi-structured interviews with harm reduction service providers and service users. These two studies lead to three main contributions, which are: (1) a set of usability problems with the software platform and various fixes for them, (2) a set of barriers and facilitators that are associated with the distributed model of drug checking and the software platform, and (3) a set of design considerations for a self-service drug checking kiosk, which is a potential future iteration of the software platform.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".