Implementation of a post-overdose quick response team in the rural Midwest: A team case study
Bibliographic record
Abstract
The opioid-involved overdose crisis in the United States has had devastating effects on communities across the country. Post-overdose outreach teams have emerged as one way to reduce overdose risk for individuals who use drugs. Limited literature exists on how these teams are developed and how they operate. Even less is known about these teams in rural locations. This case study explored one rural team’s implementation, including its strengths and barriers to serving participants. Findings from interviews with program staff indicate the team had a consistent procedure for conducting outreach with overdose survivors and family members, had broad support and buy-in from leadership, and were able to clearly articulate the program’s strengths, challenges, and opportunities for growth—including the need for more formal program evaluation. Factors that facilitated implementation included use of a person-centred and non-coercive approach, establishment of team role boundaries, multi-disciplinary collaboration, empathy, and buy-in across agencies and town leadership. Barriers included stigma among citizens, lack of an evaluation plan, difficulty providing outreach to individuals who have unstable housing, and difficulty following up with service agencies. The findings can benefit other jurisdictions, especially small and rural localities seeking to address the drug crisis more effectively.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".