Perceptions about hepatitis C and barriers and enablers to screening and treatment among Egyptian immigrants to Canada: a theory-informed qualitative study
Bibliographic record
Abstract
BACKGROUND: Despite availability of effective screening and treatment for Hepatitis C (HCV), the uptake remains suboptimal. Immigrants from HCV endemic countries comprise 35% of cases in Canada. There is an average 10-year diagnosis delay, causing poor health outcomes and high healthcare system costs. Therefore, we aimed to understand immigrants' perceptions and beliefs about HCV, as well as the barriers and enablers to HCV care among immigrants, with a focus on individuals from Egypt, given the country's high endemic rates of HCV infection and the large Egyptian community in Canada. METHODS: We established a Community Advisory Group to provide advice at all stages. We used a qualitative-descriptive design guided by the Common-Sense Self-Regulation Model and Theoretical Domains Framework to perform semi-structured interviews with adult immigrants from Egypt (with or without HCV) in Ottawa, Canada. Sampling continued until thematic saturation was achieved. The interviews were double-coded and key findings were identified. RESULTS: We conducted interviews with 18 individuals (eight females, ten males), including 12 who had undergone HCV screening. Among them, seven had tested positive, and all had received treatment. While all participants were aware of HCV, misconceptions and a lack of knowledge regarding its symptoms, modes of transmission, and treatment options were prevalent. Many stated that they would not seek screening in the absence of significant symptoms. Perceived stigma associated with HCV and experiences of ethnocultural discrimination discouraged some individuals from seeking care. Additionally, challenges such as limited access to family doctors and long wait times were frequently cited as barriers. However, those who had received HCV treatment reported positive experiences and remained engaged in follow-up care. CONCLUSION: There is an urgent need to improve access to care for immigrants from endemic countries to eliminate HCV in Canada. We took a systematic, theory-informed approach to understand lived experiences and views among Egyptian immigrants in Canada. We identified key factors contributing to the low uptake of HCV screening and treatment. These findings will inform a theory-based intervention to optimize HCV care in immigrant 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".