Stimulant safe supply: a potential opportunity to respond to the overdose epidemic
Bibliographic record
Abstract
BACKGROUND: Occurring against the backdrop of an overdose crisis, stimulant use and stimulant-involved deaths in North America are increasing at an alarming rate. Many of these deaths are being attributed to fentanyl and related analogs, which have been increasingly found within street-level stimulant supplies. Within this, people experiencing socio-economic marginalization are at the greatest risk of overdose and other harms from adulterated stimulants. Current treatments for stimulant use disorder have limited effectiveness, and even less applicability to the lived realities of marginalized stimulant users. Emerging technologies, such as drug checking, are being implemented to support safer stimulant use, but the accessibility and utility of these technologies to stimulant users are framed by experiences of vulnerability that render them largely ineffective. STIMULANT SAFE SUPPLY: Solutions that provide a legal and safe supply of non-adulterated stimulants of known quality, and within a health care framework, are needed to directly address the risk of an increasingly adulterated stimulant supply. Similar innovative opioid-focused interventions are being piloted with medications that have a similar pharmacological effect as their illicit counterparts. While there are currently no approved pharmacotherapies for stimulant use, research has demonstrated a number of stimulant medications that are promising substitutes for cocaine and methamphetamine use. Much like with opioid-focused pharmacotherapies, having a consistent and safe supply of stimulants can lead to improved health outcomes and will drastically reduce overdose risk. However, for a stimulant safe supply intervention to be a success, it must provide the high and performance-enhancing effects that people seek from the illicit market, which requires doses and user agency that trials to date have not provided. CONCLUSION: Efforts are needed to investigate the feasibility of pharmacological stimulant-based interventions that address safe supply needs. The promise of similar opioid-focused approaches in addressing both overdose-related risks and experiences related to vulnerability underscores the need to advance safe supply approaches targeted towards people who use stimulants. Given the current overdose crisis and rising stimulant use across North America, the implementation and evaluation of such novel stimulant-focused interventions should be a public health priority.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.060 | 0.009 |
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".