Coproduction of Low-Barrier Hepatitis C Virus and HIV Care for People Who Use Drugs in a Rural Community: Brief Qualitative Report
Bibliographic record
Abstract
BACKGROUND: People who inject drugs are experiencing syndemic conditions with increasing risk of infection with hepatitis C (HCV) and HIV. However, rates of accessing HCV and HIV testing and treatment among people who inject drugs are low for various reasons, including the criminalization of drug use, which leads to a focus on treating drug use rather than caring for drug users. For many people who inject drugs, health care becomes a form of structural violence, resulting in traumatic experiences, fear of police violence, unmet needs, and avoidance of medical care. There is a clear need for novel approaches to health care delivery for people who inject drugs. OBJECTIVE: This study aimed to analyze the process of a multidisciplinary team-encompassing health care professionals, community representatives, researchers, and people with lived experience using drugs-that was formed to develop a deep understanding of the experiences of people who inject drugs and local ecosystem opportunities and constraints to inform the cocreation of low-barrier, innovative HCV or HIV care in a rural community. Given the need for innovative approaches to redesigning health care, we sought to identify challenges and tensions encountered in this process and strategies for overcoming these challenges. METHODS: Analysis was based on an in-depth review of meeting notes from the project year, followed by member-checking with the project team to revise and expand upon the challenges encountered and strategies identified to navigate these challenges. RESULTS: Challenges and tensions included: scoping the project, setting the pace and urgency of the work, adapting to web-based work, navigating ethics and practice of payment, defining success, and situating the project for sustainability. Strategies to navigate these challenges included: dedicated effort to building personal and meaningful connections, fostering mutual respect, identifying common ground to make shared decisions, and redefining successes. CONCLUSIONS: While cocreated care presents challenges, the resulting program is strengthened by challenging assumptions and carefully considering various perspectives to think creatively and productively about solutions.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".