Educational interventions to improve hepatitis C testing or treatment in South Asian communities: A systematic review
Bibliographic record
Abstract
BACKGROUND: Hepatitis C (HCV) places a disproportionately higher burden on the South Asian community in comparison to the general population, despite the availability of effective antiviral therapies. This study seeks to characterize the effectiveness of health promotion initiatives aimed at South Asians to improve HCV prevention, education, screening, and treatment adherence. METHODS: A systematic review (PROSPERO: CRD42021253796) was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Ovid MEDLINE, CINAHL Plus, Web of Science, ERIC, Ovid Embase, Cochrane Library, and PsycINFO were searched from inception to 15 April 2022 for original studies that reported on any health promotion initiative directed at improving HCV outcomes in the South Asian community. Risk of bias was assessed via a quality score. RESULTS: A total of 15 studies (6 uncontrolled interventional, 3 before-after interventional, 3 randomized controlled, 2 prospective cohort, and 1 historically controlled interventional study designs) involving 69,958 participants were included. The most studied interventions were formal HCV teaching ( n = 12), community outreach ( n = 6), and coupling screening/testing with existing programs ( n = 3). 92% (14/15) of interventions were concluded to be successful, and 71% (10/14) of those were concluded to be feasible and/or cost-effective. CONCLUSIONS: Interventions that aim to improve HCV education and accessibility to screening/treatment can substantially reduce barriers to care in South Asian communities. Further research, of higher quality RCT evidence, is needed to study the long-term reduction in HCV prevalence from these proposed interventions, and their associated feasibility profiles.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".