The Evaluation of a Drug Checking Software Platform that Enables Remote Point-of-Care Drug Checking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In April 2016, drug-related overdoses were declared a public health emergency in British \nColumbia, Canada. At the heart of this public health emergency is fentanyl, a synthetic \nopioid and the most commonly detected drug in illicit drug toxicity deaths. However, the \nillicit drug supply as a whole has become increasingly unpredictable, especially since the \nCOVID-19 pandemic disrupted British Columbia’s drug supply, leading to complex drug \nsamples containing benzodiazepines and nitazenes, overdose on which is not reversed by \nnaloxone, the opioid overdose reversal drug as they are not opioids. One harm reduction \nresponse to the overdose crisis is drug checking, a process in which a sample of an illicit \ndrug is analyzed to determine its chemical composition. However, access to drug checking \nis not universal, and the implementation of drug checking services is hindered by several \nbarriers, such as the need for skilled technicians to analyze drug checking data. In this \nthesis, I describe research I conducted to evaluate a drug checking software platform that \nfacilitates the distributed drug checking model, a model by which drug checking is performed \nwithout skilled technicians being geographically present. The research conducted in this \nthesis comprises two studies: a heuristic evaluation of the software and semi-structured \ninterviews with harm reduction service providers and service users. These two studies lead \nto three main contributions, which are: (1) a set of usability problems with the software \nplatform and various fixes for them, (2) a set of barriers and facilitators that are associated \nwith the distributed model of drug checking and the software platform, and (3) a set of \ndesign considerations for a self-service drug checking kiosk, which is a potential future \niteration of the software platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it