Bibliographic record
Abstract
ObjectivesWith nearly 50,000 people having died since 2016 as a result of the unregulated toxic drug supply, novel approaches to care are needed. A small number of Safer Stimulant Supply programs have been piloted in Canada, which seek to provide a pharmaceutical-grade stimulant medication replacement for the toxic unregulated stimulant supply. In this paper, we describe the results of retrospective Safer Stimulant Supply program medical chart reviews.MethodsWe extracted data from program intake and check-in forms collected on an ongoing basis by the clinical teams. In total, 28 medical charts were included in this evaluation. Data collected was reported using descriptive statistics.ResultsChart reviews showed that over the course of the program, program participants reported an overall decrease in their unregulated stimulant use. Specifically, at program intake and check-in appointments, cocaine use went from a median of 10 points/day to 0 points/day, and crystal methamphetamine use went from a median of 1.5 points/day to 0 points/day. Chart reviews also showed that program participants reported increased access to primary care and infectious disease programs and improvements in housing status.ConclusionsOur research demonstrated that program participants found Safer Stimulant Supply programs to be impactful in addressing ongoing drug use. Safer Stimulant Supply programs remain an underutilized but important harm reduction tool to address the drug poisoning crisis.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".