Naloxone urban legends and the opioid crisis: what is the role of public health?
Bibliographic record
Abstract
As the overdose crisis in North America continues to deepen, public health leaders find themselves responding to sensational media stories, many of which carry forms and themes that mark them as urban legends.This article analyzes one set of media accounts - stories of misuse of naloxone, an opioid overdose antidote distributed to people who use drugs - through the lens of social science scholarship on urban legends. We suggest that these stories have met a public need to feel a sense of safety in uncertain times, but function to reinforce societal views of people who use drugs as undeserving of support and resources.Our field has a duty to speak out in favour of evidence-based programs that support the health of people who use drugs, but the optimal communication strategies are not always clear. Drawing attention to the functions and consequences of urban legends can help frame public health communication in a way that responds to needs without reinforcing prejudices, with application beyond naloxone to the other urban legends that continue to emerge in response to this 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".