890. Bridging the Gap to Help Address the Opioid Crisis: A Novel Model of Care to Integrate Substance Use, Mental Health, and Infectious Disease Services
Bibliographic record
Abstract
Abstract Background There is a converging public health crisis as the opioid epidemic and increased injection drug use is driving rates of infectious diseases. Multidisciplinary care, integrating infectious diseases, substance use, and mental health services, is crucial to address this crisis. This study evaluated a novel rapid access care model to improve treatment access for opioid use, mental health, and related infectious diseases. Methods The Rapid Access Addiction Medicine (RAAM) clinic is a multidisciplinary, walk-in care model located in a mental health center in Ottawa, Canada. RAAM provides collaborative, inter-agency care, with rapid access to care facilitated through seamless care pathways (i.e., from the emergency department). RAAM offers substance use and mental health treatment, screening and care for infectious diseases, harm reduction, and connection to community services. RAAM patients (N = 411) presenting between April 2018 and January 2019 completed substance use and mental health measures upon intake and 30-day follow-ups. Clinical information was collected via chart review. Results Of the total sample, 20% (n = 83; 66% men) had problematic opioid use. Most patients reported high opioid dependence severity (97%), injection drug use (67%), and polysubstance use (97%), including cocaine (62%), alcohol (40%), and amphetamines (35%). Most patients reported anxiety (86%) and depression (75%). The number of patients tested for HIV, HCV, HBV, and other STIs was 29%, 27%, 28%, and 24%, respectively. Most patients tested (61%) were young adults (aged 16–29). Of those tested, 15% tested positive for HCV and treatment initiation was facilitated for 66% of patients (33% resolved spontaneously). At 30-day follow-up, patients showed significantly reduced substance use and improved depression and anxiety (Ps < 0.05). Conclusion Patients with problematic opioid use have multiple comorbidities, including undiagnosed infectious diseases; thus, highlighting the need for integrated care models like RAAM. Substance use treatment is an opportune setting to identify and treat infectious diseases in order to improve outcomes and reduce disease transmission. Leadership from infectious disease specialists is key to this successful integration. Disclosures All Authors: No reported Disclosures.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".