Factors associated with knowledge and awareness of Hepatitis B in individuals of Chinese descent: Results from a mass point of care testing and outreach campaign in Toronto, Canada
Bibliographic record
Abstract
Background: Migrants from hepatitis B virus (HBV) endemic regions are at high risk of having chronic infection. Despite this, HBV knowledge and awareness programming, and low-barrier screening methods such as point of care (POC) testing, among this group have yet to become routine. Methods: We conducted a mass HBV POC screening and knowledge and awareness campaign for individuals of Chinese descent in Toronto, Canada. POC screening was administered, then participants completed a knowledge questionnaire. Logistic regression identified associations between demographic factors and participants’ level of HBV knowledge. Results: From 2015 to 2018, 33 outreach events resulted in 891 individuals completing testing and the knowledge questionnaire. Individuals averaged 64.4 years old. Most, 62% ( N = 552), were female, and 73.6% ( N = 656) have been in Canada for <30 years. The average questionnaire score was 70.7% correct, with 65.2% ( N = 581) demonstrating a high level of HBV knowledge. Post-secondary education (OR: 2.19, 95% CI: 1.41, 3.39), income of $50,000 to <$75,000 (OR: 2.74, 95% CI: 1.39, 5.43), and having familial history of HBV (OR: 1.72, 95% CI: 1.06, 2.78) were associated with high knowledge. The observed prevalence of HBV was 1.5%, with 13 individuals testing positive on the POC test and confirmatory laboratory testing. Conclusions: Improving knowledge and awareness of HBV is critical to empowering people, especially migrants who experience barriers to care, to pursue vaccination, testing, and treatment. Combining knowledge outreach and POC test campaigns, enabled discussion and screening for HBV with large numbers of people, and can be tailored for optimal effectiveness for specific groups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".