Fostering Patient-Clinician Communication to Promote Rapid HIV, Hepatitis B Virus, and Hepatitis C Virus Diagnostic Testing: Conceptual Development of a Multilingual App
Bibliographic record
Abstract
BACKGROUND: Migrants are disproportionately affected by HIV, hepatitis B virus (HBV), and hepatitis C virus (HCV). Clinicians, at times, fail to offer rapid diagnostic testing (RDT) for these viruses when a language barrier exists in the patient-clinician relationship, therefore creating missed testing opportunities. Although their effectiveness has been demonstrated elsewhere, conventional, in-person interpreters are costly and underused in practice. Furthermore, clinicians often call upon ad hoc interpreters, which introduces complexities in the clinical relationship. Digital solutions exist to diminish the burden of language barriers; however, the challenges of developing a multilingual and multicultural app have yet to be documented with respect to RDT in the nonfrancophone migrant population in France. OBJECTIVE: Our goal was to design a multilingual app to overcome language barriers, health literacy barriers, and fears related to being tested to promote RDT of HIV, HBV, and HCV in the nonfrancophone migrant population in France. METHODS: A combination of qualitative methods, agile development, and user-centered design was used. We conducted 2 focus groups (FGs) with 12 participants, including physicians, nurses, and social workers conducting RDT, as well as 1 modified Delphi survey with 68 participants including physicians and nurses. FGs explored the content (risk factors and medical history), functions (cultural adaptation and instant translation), and interface ergonomics (graphics and font) needed in the app. The Delphi presented 95 content items that the researchers sought to include in the app. RESULTS: Using FGs to inform the Delphi survey, we scientifically determined the app's content consisting of 95 items using expert consensus, developed a mock-up, and conducted initial user testing. We created an app that contains both migrant and clinician interfaces and includes a sociodemographic, risk assessment, health literacy, and testing barrier questionnaires available in 11 languages. Educational content is related to HIV, HBV, and HCV, along with the ability to understand whether the migrant agrees to be tested. CONCLUSIONS: This study allowed us to conceptualize a multilingual app that aims to increase the acceptance of RDT for HIV, HBV, and HCV. The specific features of the Assistant intelligent au dépistage des allophones app were designed to overcome the testing barriers in the nonfrancophone migrant population. The next phase will be an implementation study, as we intend to validate our app.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".