Overdose Education and Naloxone Distribution Programmes and the Ethics of Task Shifting
Bibliographic record
Abstract
Abstract North America is in the grips of an epidemic of opioid-related poisonings. Overdose education and naloxone distribution (OEND) programmes emerged as an option for structurally vulnerable populations who could not or would not access mainstream emergency medical services in the event of an overdose. These task shifting programmes utilize lay persons to deliver opioid resuscitation in the context of longstanding stigmatization and marginalization from mainstream healthcare services. OEND programmes exist at the intersection of harm reduction and emergency services. One goal of OEND programmes is to help redress the health-related inequities common among people who use drugs, which include minimizing the gap between people who use drugs and the formal healthcare system. However, if this goal is not achieved these inequities may be entrenched. In this article, we consider the ethical promises and perils associated with OEND as task shifting. We argue that public health practitioners must consider the ethical aspects of task shifting programmes that may inadvertently harm already structurally vulnerable populations. We believe that even if OEND programmes reduce opioid-related deaths, we nevertheless question if, by virtue of its existence, OEND programmes might also unintentionally disenfranchise structurally vulnerable populations from comprehensive healthcare services, including mainstream emergency care.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".