RREACT: A mobile multidisciplinary response to overdose
Bibliographic record
Abstract
Opioid overdose is a leading cause of death in the United States, and engaging with patients following overdose to provide harm reduction and recovery resources can prove difficult. Quick response models use mobile, multidisciplinary teams to establish a time-sensitive connection between individuals who overdosed and harm reduction and recovery resources that improve outcomes. These quick response models are consistent with the broader field of mobile-integrated health programs that are growing in number and acceptability, though the literature base is sparse and programs vary. We describe the 5-year reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework of the Rapid Response Emergency Addiction and Crisis Team (RREACT), a fire/emergency medical services-led, multidisciplinary (firefighter/paramedic, law enforcement officer, social worker) mobile outreach team. RREACT provides harm reduction, linkage/transportation to care and wrap-around services to individuals following a nonfatal opioid overdose that resulted in an emergency response in Columbus, Franklin County, Ohio, United States. Between 2018 and 2022, RREACT made 22,157 outreach attempts to 11,739 unique patients. RREACT recorded 3,194 direct patient contacts during this time, resulting in 1,200 linkages to care: 799 direct transports to opioid use disorder treatment and 401 warm handoffs to community treatment agencies. Furthermore, RREACT's staffing increased from 4 full-time equivalent staff in 2018 to 15.5 in 2022 and was supported by the surrounding community through 287 community outreach events and the development of an alumni program. These preliminary results further support the deployment of multidisciplinary mobile outreach teams to increase access to harm reduction and recovery resources following opioid overdose.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".