Understanding Users’ Engagement in a Provider-Created Mobile App for Training to Advance Hepatitis C Care: Knowledge Assessment Survey Study
Bibliographic record
Abstract
BACKGROUND: The World Health Organization and the Centers for Disease Control and Prevention have set ambitious hepatitis C virus (HCV) elimination targets for 2030. Current estimates show that the United States is not on pace to meet elimination targets due to multiple patient, clinic, institutional, and societal level barriers that contribute to HCV testing and treatment gaps. Among these barriers are unawareness of testing and treatment needs, misinformation concerning adverse treatment reactions, need for substance use sobriety, and treatment efficacy. Strategies to improve viral hepatitis education are needed. OBJECTIVE: We aim to provide a high-quality HCV educational app for patients and health care workers, particularly nonprescriber staff. The app was vetted by health care providers and designed to guide users through the HCV testing and treatment stages in a self-exploratory way to promote engagement and knowledge retention. The app is comprised of five learning modules: (1) Testing for Hep C (hepatitis C), (2) Tests for Hep C Positive Patients, (3) Treatments Available to You, (4) What to Expect During Treatment, and (5) What to Expect After Treatment. METHODS: An HCV knowledge assessment survey was administered to providers and patients at the Yale School of Medicine and 11 Connecticut HIV clinics as part of a grant-funded activity. The survey findings and pilot testing feedback guided the app's design and content development. Data on app usage from November 2019 to November 2022 were analyzed, focusing on user demographics, engagement metrics, and module usage patterns. RESULTS: There were 561 app users; 216 (38.5%) accessed the training modules of which 151 (69.9%) used the app for up to 60 minutes. Of them, 65 (30.1%) users used it for >60 minutes with a median time spent of 5 (IQR 2-8) minutes; the median time between initial accession and last use was 39 (IQR 18-60) days. Users accessed one or more modules and followed a nonsequential pattern of use: module 1: 163 (75.4%) users; module 4: 82 (38%); module 5: 67 (31%); module 3: 49 (22.7%); module 2: 41 (19%). CONCLUSIONS: This app, created in an academic setting, is one of a few available in English and Spanish that provides content-vetted HCV education for patients and health care supportive staff. It offers the convenience of on-demand education, allowing users to access crucial information about HCV management and treatment in a self-directed fashion that acknowledges and promotes variable preferences in learning approaches. While app uptake was relatively limited, we propose that future efforts should focus on combined promotion efforts with marketing strategies experts aligned with academic experts. Incorporating ongoing user feedback and integrating personalized reminders and quizzes, will further enhance engagement, supporting the broader public health HCV elimination goals.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".