Innovative Nurse-Led Community Health Centre–Corrections Partnership for Hepatitis C Testing and Treatment in Victoria, British Columbia
Bibliographic record
Abstract
People who are incarcerated experience a high rate of hepatitis C (HCV) worldwide, and HCV micro-elimination in prisons is an effective strategy to support treatment. In Victoria, British Columbia, administrative barriers limited HCV testing and treatment at Vancouver Island Correctional Centre (VIRCC), and people who were HCV RNA+ were lost to follow up. Cool Aid Community Health Centre (CACHC) is an inner-city, primary care clinic that serves a marginalized population. The CACHC HCV nurse coordinator with the VIRCC nurse held HCV testing 'blitzes' at VIRCC and offered phlebotomy for screening and pre-treatment bloodwork. Clients who tested HCV RNA+ were started on HCV treatment and if discharged before completion, CACHC followed them in the community. A retrospective chart review was conducted to identify all clients who accessed HCV testing and treatment through the VIRCC partnership. To date, 230 clients were tested: 49 tested HCV antibody+, 11 tested HCV RNA+, and 10 started on treatment (6 SVR). Case management and consultation with the nurse coordinator and VIRCC nurse supported treatment starts for an additional 18 clients (14 SVR). This pragmatic and innovative approach to HCV care with people who are incarcerated demonstrated effective HCV testing and treatment. CACHC and VIRCC have established closer relationships and reduced barriers to reach and maintain continuity with this target population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".