Addressing digital exclusion to improve access to HIV and viral hepatitis care for people who experience criminalization: a mixed methods evaluation of a quality improvement project
Bibliographic record
Abstract
BACKGROUND: People who experience criminalization, such as those who use drugs, are incarcerated, and are affected by homelessness, have a high prevalence of HIV and/or hepatitis C virus (HCV) infection and low treatment uptake in British Columbia. Barriers to care include unreliable means of maintaining contact with healthcare providers. To reduce these barriers, the Test, Link, Call (TLC) Project provides cell phones and peer health mentors to support access to HIV and/or HCV care. This study aims to determine the outcomes and acceptability of TLC and its impact on care engagement. METHODS: A mixed-methods evaluation was conducted over the first 29 months (October 2021–March 2024) of the TLC Project. Data were collected concurrently in two rounds: the first after one year and the second two years after launch. Qualitative data were collected using semi-structured interviews conducted with healthcare providers (n = 8), peer health mentors (n = 6), and program participants (n = 20). Quantitative data, including demographic and clinical information, were gathered through program records and cross-sectional clinical chart reviews. Factors associated with HCV treatment uptake were assessed among HCV RNA positive participants (n = 245) using multivariate logistic regression. Data from both rounds were integrated for comprehensive analysis. RESULTS: 273 participants were enrolled in HCV care, and 26 in HIV care. Interviewees found TLC highly acceptable and effective. Positive outcomes included increased access to health and social services, connection to loved ones, independence, and safety. Challenges included phone theft and digital literacy issues. Overall, 57% of TLC participants enrolled for HCV care initiated curative treatment, compared to 40% among people who currently inject drugs in the provincial administrative database in 2020. The multivariate logistic regression analysis suggested that gender, housing stability, safer supply prescriptions, and length of involvement in the TLC program are predictive factors influencing treatment initiation. CONCLUSIONS: The provision of cell phones and peer health mentors effectively increased engagement in HIV and HCV care, demonstrating substantial benefits despite some challenges. This cost-effective intervention could be expanded to support people who experience criminalization in other geographic locations and addressing other health conditions, such as syphilis and substance use disorder.
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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 | 0.084 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".