Harm reduction in the Heartland: public\nknowledge and beliefs about naloxone\nin Nebraska, USA
Bibliographic record
Abstract
Background: Opioid-related overdose deaths have been increasing in the United States (U.S.) in the last twenty years, creating a public health challenge. Take-home naloxone is an effective strategy for preventing opioid-related overdose death, but its widespread use is particularly challenging in smaller cities, towns, and rural areas where it may be stigmatized and/or poorly understood. Methods: We analyzed data on knowledge and beliefs about drug use and naloxone among the general public in Nebraska, a largely rural state in the Great Plains region of the U.S., drawing on the 2020 Nebraska Annual Social Indicators Survey. Results: Respondents reported negative beliefs about people who use drugs (PWUD) and little knowledge of naloxone. Over half reported that members of their community view PWUD as blameworthy, untrustworthy, and dangerous. Approximately 31% reported being unaware of naloxone. Only 15% reported knowing where to obtain naloxone and less than a quarter reported knowing how to use it. Knowing where to obtain naloxone is associated with access to opioids and knowing someone who has recently overdosed, but having ever used opioids or being close to someone who uses opioids is not associated with naloxone knowledge. Finally, almost a quarter of respondents endorsed the belief that people who use opioids will use more if they have access to naloxone. Conclusion: Our findings highlight stigmatizing beliefs about PWUD and underscore the need for further education on naloxone as an effective strategy to reduce opioid-related overdose death. We highlight the implications of these findings for public education efforts tailored to non-urban communities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".