Increasing access: Making naloxone available at highway rest areas
Bibliographic record
Abstract
Background: Naloxone is one of the most successful drugs in reversing the pharmacological effects of opioids and, in turn, preventing overdose. Increasing naloxone availability is an effective way to combat opioid-related overdose deaths. Recent changes in legislation across the United States have provided the jurisdiction to make naloxone more readily available. Naloxboxes are transparent, unsecured containers stocked with naloxone that are strategically placed in semi-private public spaces, such as restrooms. Objective: To assess the effectiveness of installing naloxboxes at highway rest areas in Ohio as a strategy to increase public access to naloxone. This assessment draws on the pilot partnerships with the Ohio Department of Transportation, emergency medical services, and public health agencies, and explores implications for broader community implementation. Methods: In collaboration with existing Ohio organizations, the HEALing Communities Study leveraged local and national funding to facilitate the expansion of naloxone use through the deployment of naloxone boxes at Ohio highway rest areas. Results: Naloxboxes were found to be well accepted by the public and sustainable in highway rest areas. Their successful implementation and ongoing maintenance relied on multisectoral support, requiring collaboration across community organizations, public health agencies, and other stakeholders. Conclusion: This innovative approach promoted the widespread distribution of naloxone while still preserving anonymity.
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".