Combined COVID-19 vaccination and hepatitis C virus screening intervention in marginalised populations in Spain
Bibliographic record
Abstract
BACKGROUND: COVID-19 has hindered hepatitis C virus (HCV) and HIV screening, particularly in marginalised groups, who have some of the highest rates of these conditions and lowest rates of COVID-19 vaccination. We assessed the acceptability of combining HCV testing with COVID-19 vaccination in a centre for addiction services (CAS) in Barcelona and a mobile testing unit (MTU) in Madrid, Spain. METHODS: From 28/09/2021 to 30/06/2022, 187 adults from marginalised populations were offered HCV antibody (Ab) testing along with COVID-19 vaccination. If HCV Ab+, they were tested for HCV-RNA. MTU participants were also screened for HIV. HCV-RNA+ and HIV+ participants were offered treatment. Data were analysed descriptively. RESULTS: Findings show how of the 86 CAS participants: 80 (93%) had been previously vaccinated for COVID-19, of whom 72 (90%) had the full first round schedule; none had a COVID-19 vaccine booster and all received a COVID-19 vaccine; 54 (62.8%) were tested for HCV Ab, of whom 17 (31.5%) were positive, of whom all were tested for HCV-RNA and none were positive. Of the 101 MTU participants: none had been vaccinated for COVID-19 and all received a COVID-19 vaccine; all were tested for HCV Ab and HIV and 15 (14.9%) and 9 (8.9%) were positive, respectively; of those HCV Ab+, 9 (60%) were HCV-RNA+, of whom 8 (88.9%) have started treatment; 5 (55.6%) of those HIV+ had abandoned antiretroviral therapy, of whom 3 (60%) have re-started it. CONCLUSIONS: The intervention was accepted by 54 (62.8%) CAS participants and all MTU participants and can be used in marginalised communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".