Addressing hepatitis C in the foreign-born population: A key to hepatitis C virus elimination in Canada
Bibliographic record
Abstract
Hepatitis C virus (HCV) is the leading cause of death from infectious disease in Canada. Immigrants are an important group who are at increased risk for HCV; they account for a disproportionate number of all HCV cases in Canada (~30%) and have approximately a twofold higher prevalence of HCV (~2%) than those born in Canada. HCV-infected immigrants are more likely to develop cirrhosis and hepatocellular carcinoma and are more likely to have a liver-related death during a hospitalization than HCV-infected non-immigrants. Several factors, including lack of routine HCV screening programs in Canada for immigrants before or after arrival, lack of awareness on the part of health practitioners that immigrants are at increased risk of HCV and could benefit from screening, and several patient- and health system-level barriers that affect access to health care and treatment likely contribute to delayed diagnosis and treatment uptake. HCV screening and engagement in care among immigrants can be improved through reminders in electronic medical records that prompt practitioners to screen for HCV during clinical visits and implementation of decentralized community-based screening strategies that address cultural and language barriers. In conclusion, early screening and linkage to care for immigrants from countries with an intermediate or high prevalence of HCV would not only improve the health of this population but will be key to achieving HCV elimination in Canada. This article describes the unique barriers encountered by the foreign-born population in accessing HCV care and approaches to overcoming these barriers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".