An Ethical Analysis of Safe Supply
Bibliographic record
Abstract
Opioid overdose deaths in the United States have been steadily increasing for decades. Initially, these deaths were driven by overdoses from prescription opioids. Strict limits were placed on opioid prescriptions to decrease the supply of available opioids. Instead, this prompted a shift toward the illicit opioid market, causing an increase in heroin-related overdoses. Fentanyl, a synthetic opioid that is more potent than heroin, has become commonplace in the illicit supply of opioids. The illicit opioid market is unregulated and unpredictable, and there is no way to know exactly what is in a bag sold as heroin or “dope”. Illicit drug use has been historically dealt with as a crime rather than a public health issue in the United States. Recently, harm reduction has been offered as an alternative to this punitive approach. Harm reduction is a set of practical strategies and ideas aimed at reducing negative consequences associated with drug use. Naloxone distribution and syringe service programs are examples of currently utilized harm reduction strategies in the United States. While these programs are necessary to improve the quality of life of people who use illicit drugs, the rates of death from overdose are continuing to increase. These strategies do not protect people from the toxic and unpredictable drug supply. Safe supply is a relatively new concept, but there have been some small-scale implementations of this practice in Canada. Safe supply refers to a legal and regulated supply of drugs with mind and body-altering properties that traditionally have been accessible only through the illicit drug market. This is a necessary strategy to combat the alarming rise in overdose mortality. In this paper, I will analyze the ethics of this strategy using a principalism approach. This analysis concludes that safe supply is ethically sound, and it should be a part of our approach to the overdose epidemic. Safe supply promotes autonomy, prevents harms, advances well-being, and upholds justice for people who use drugs.
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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 | 0.043 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".