Treating people where they are: Nurse‐led micro‐elimination of hepatitis C in supported housing sites for networks of people who inject drugs in Victoria, Canada
Bibliographic record
Abstract
To achieve the World Health Organization's goal of eliminating hepatitis C (HCV) by 2030 requires enhanced HCV testing and treatment among people who use drugs (PWUD). Micro-elimination of HCV is a strategy to target HCV testing and treatment efforts to specific segments of the population. From February to December 2018 nurses initiated a "seek & treat" micro-elimination approach, increasing outreach and removing barriers to accessing HCV treatment in a clinic setting by testing and treating individuals, including PWUD, where they live. The aim of this study was to evaluate the proportion of clients with HCV antibodies and HCV RNA and the response to direct acting agent (DAA therapy) among people who live at or have social connections to local supportive housing sites through this nurse-led micro-elimination project in Victoria, Canada. A chart review of electronic medical records and case management documentation was used to collect relevant data of participants treated with DAA therapy, identified through specific housing site testing and outreach interventions. In total, 180 people were tested for HCV antibodies, 72 (40%) were antibody positive: 51 (28%) were RNA positive, 13 (7%) had spontaneously cleared and 8 (4%) had been previously treated. Of the 51 that were currently living with HCV, 43 people were started on treatment, 39 have achieved sustained virologic response (SVR). By providing treatment to clients in their homes and with their friends, clinicians have been able to treat clients, including those with limited contact with the health care system.
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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 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".