Introducing Public Health Vending Machines in Rural Communities: Protocol for a Study Using a Community-Based Participatory Approach
Bibliographic record
Abstract
BACKGROUND: Drug-related overdoses impact communities all over the United States. In the past 2 decades, over 28,000 people have died of a drug overdose in North Carolina (NC). Research has shown that there has been an increase in overdose deaths throughout NC, particularly in rural areas. To reduce overdose rates, health care interventions should be expanded. Naloxone distribution is one intervention to combat overdose rates. Naloxone is a medication designed to reverse an opioid overdose rapidly. Public health vending machines (PHVMs) are a strategy recently implemented in some US communities to expand access to harm reduction supplies. Examples of locations where PHVMs have been installed include public health departments, libraries, county detention centers, and law enforcement offices. OBJECTIVE: This protocol aims to develop a community-engaged approach to implementing PHVMs as a health care delivery option for harm reduction supplies in 5 rural counties in NC. METHODS: This study will use a community-based participatory approach in which we partnered with the NC Harm Reduction Coalition and Community Impact NC to engage with substance use prevention providers and community members in 5 rural counties in NC to improve naloxone access. We will collect qualitative interview data from people with lived experience of substance use to identify the optimal placement of PHVMs and items to be stocked in PHVMs. To do this, we will hire 1 local community member with lived experience of substance use from each county to be an interviewer who will recruit, conduct interviews, and collect data from other community members with lived experience of substance use. Interviewers will be trained to recruit participants, conduct interviews, and collect and analyze data. Developing a protocol for training interviewers includes an interview training presentation with an adapted collaborative institutional training initiative portion. RESULTS: Data will be collected from 2024 to 2025. The findings will inform the implementation of PHVMs to improve harm reduction access and assist in decreasing overdose deaths. CONCLUSIONS: This study will use in a community-based participatory approach to improve naloxone access in rural communities. Community partners will assist the academic team in developing a sustainability plan for each county and an implementation toolkit for other communities to use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/64913.
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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 | 0.086 | 0.067 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.105 | 0.023 |
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".