Harm reduction and recovery services support (HRRSS) to mitigate the opioid overdose epidemic in a rural community
Bibliographic record
Abstract
BACKGROUND: Rural areas in the United States (US) are ravaged by the opioid overdose epidemic. Oconee County, an entirely rural county in northwest South Carolina, is likewise severely affected. Lack of harm reduction and recovery resources (e.g., social capital) that could mitigate the worst outcomes may be exacerbating the problem. We aimed to identify demographic and other factors associated with support for harm reduction and recovery services in the community. METHODS: The Oconee County Opioid Response Taskforce conducted a 46-item survey targeting a general population between May and June in 2022, which was mainly distributed through social media networks. The survey included demographic factors and assessed attitudes and beliefs toward individuals with opioid use disorder (OUD) and medications for OUD, and support for harm reduction and recovery services, such as syringe services programs and safe consumption sites. We developed a Harm Reduction and Recovery Support Score (HRRSS), a composite score of nine items ranging from 0 to 9 to measure level of support for placement of naloxone in public places and harm reduction and recovery service sites. Primary statistical analysis using general linear regression models tested significance of differences in HRRSS between groups defined by item responses adjusting for demographic factors. RESULTS: There were 338 survey responses: 67.5% were females, 52.1% were 55 years old or older, 87.3% were Whites, 83.1% were non-Hispanic, 53.0% were employed, and 53.8% had household income greater than US$50,000. The overall HRRSS was relatively low at a mean of 4.1 (SD = 2.3). Younger and employed respondents had significantly greater HRRSS. Among nine significant factors associated with HRRSS after adjusting for demographic factors, agreement that OUD is a disease had the greatest adjusted mean difference in HRSSS (adjusted diff = 1.22, 95% CI=(0.64, 1.80), p < 0.001), followed by effectiveness of medications for OUD (adjusted diff = 1.11, 95%CI=(0.50, 1.71), p < 0.001). CONCLUSIONS: Low HRRSS indicates low levels of acceptance of harm reduction potentially impacting both intangible and tangible social capital as it relates to mitigation of the opioid overdose epidemic. Increasing community awareness of the disease model of OUD and the effectiveness of medications for OUD, especially among older and unemployed populations, could be a step toward improving community uptake of the harm reduction and recovery service resources critical to individual recovery efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".