Efficacy of a Mobile Texting App (HepTalk) in Encouraging Patient Participation in Viral Hepatitis B Care: Development and Cohort Study
Bibliographic record
Abstract
BACKGROUND: Chronic hepatitis B virus (HBV) infection is a major cause of liver-related morbidity and mortality among Asian Americans in the United States. Despite the available resources, a majority of HBV-infected individuals are not able to access adequate health care owing to numerous barriers. OBJECTIVE: This study aimed to assess the efficacy of a newly developed mobile texting app (HepTalk) in overcoming these barriers and improving patient engagement and health care access among HBV-infected and nonimmune individuals. METHODS: HepTalk was employed for two-way communication between participants and patient navigators. A total of 82 Korean American participants who were either HBV infected or nonimmune to HBV, identified from a community hepatitis B campaign in New York, were enrolled in the study. After informed consent was obtained, both the frequency and themes of the text messages were evaluated. The effects of this communication on linkage to care at the end of the 6-month intervention period were analyzed and discussed. RESULTS: On average, patient navigators sent and received 14 and 8 messages per participant, respectively, during the 6-month period. The themes of the messages were similar to the following 4 categories: finding providers, scheduling appointments with providers, health education, and financial issues. Of the 82 participants, 78 were linked to care within 6 months (a 95% linkage rate). CONCLUSIONS: HepTalk may be employed as an effective and strategic tool to facilitate communicative interaction between patients and patient navigators or health care providers, thereby improving patient engagement and health care access.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".