Evaluating Clinician Expectations of mHealth Solutions to Increase Rapid-Screening for HIV and Hepatitis in Migrant Populations in France: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Migrants underuse screening opportunities for HIV, hepatitis B, and hepatitis C despite elevated risk factors for contracting these infections. Language barriers are an often given as reasons for limiting access to services. Translation and communication apps increase communication and overall patient satisfaction in the patient-provider relationship. In the development and adoption of new technology, expectations play an important role. OBJECTIVE: This study aimed to explore health care professionals' opinions and attitudes regarding their screening practices with migrants and their expectations for a new communication tool that could improve migrants' screening use. METHODS: In this qualitative study, a purposive (diverse) sampling method was used to invite doctors and nurses who conduct rapid screening tests with migrants from 4 centers of the French Office of Immigration and Integration in 3 geographic regions of France. Semistructured interviews were conducted to survey their opinions on the rapid testing of migrants, the use of telephone interpreters, the concept of health literacy, and their expectations of a new communication tool that could overcome language barriers and promote rapid screening in the new migrant population. RESULTS: In all, 20 interviews were conducted with 11 doctors and 9 nurses with a median age of 58 (range 25-67) years. Participants favored the integration of an innovative communication tool in the context of rapid screening of migrants. However, there were concerns related to the implementation and added value of the tool while migrants were already reluctant to be screened. Expectations were for a tool that would present information in simplified French or a chosen language but also supports a positive attitude toward screening. Health professionals also expressed the wish that the technology could help with the collection of health data. CONCLUSIONS: Feedback from health professionals provides a better understanding of potential formats, characteristics, functions, content, and use of an innovative, digital method to communicate with migrants with limited French proficiency. Findings contribute to the conceptual development of an electronic app and its implementation within the ApiDé study, which aims to validate a digital app to address language barriers to increase the use of screening among migrants with limited French proficiency in France.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".