Cultural Insight: AI Qualitative Analysis of Leprosy

The November 11, 2026 study, “Kokobe” and “gwasi”: Perceptions of leprosy in French Guiana, found that local language, spiritual explanations, stigma, and treatment experiences shaped how people understood leprosy and navigated care.

AI qualitative analysis can help public health teams synthesize multilingual interviews, connect local terms to broader themes, and compare patient and community perspectives, while human researchers retain responsibility for cultural interpretation and participant protection.

• Between April and September 2025, the study interviewed 20 patients and 37 community members in French Guiana across 37 interviews.
• The study found that Maroon and Haitian participants often emphasized spiritual explanations such as taboo transgression or curses, while bacterial causation was rarely mentioned.
• The terms “kokobe” and “gwasi” described culturally specific understandings of visible lesions and deformities, but the November 2026 study warned that these terms do not correspond exactly to the biomedical category of leprosy.
• The study reported that local plant-based remedies were widely used, but the remedies did not appear to reduce adherence to multidrug therapy among participants.
• The researchers concluded in November 2026 that culturally sensitive communication should clarify person-to-person transmission while acknowledging local terminology and practices.

The researchers used semi-structured interviews and thematic analysis to examine illness perceptions, explanatory models, stigma, socioeconomic effects, and care pathways, according to the November 2026 PLOS study.

The study included 20 adult patients recruited through French Guiana health centres and 37 community members from villages in Western French Guiana, according to the study methods published in November 2026. Recruitment continued until thematic saturation, meaning successive interviews produced no substantially new themes or explanatory models.

The research team combined deductive coding, based on the interview guide and the Knowledge, Attitudes and Practices framework, with inductive coding to identify emergent cultural meanings, according to the November 2026 study.

The researchers manually transcribed interviews, organized codes and quotations in Excel, and reviewed interpretations through discussions among dermatologists, an anthropologist, and a Maroon cultural health mediator, according to the study published in November 2026.

The study also illustrates why qualitative analysis matters in health research. One community member told the researchers, “It starts with spots... If you don't treat it, your fingers can bend.” The quotation shows how local descriptions can connect visible symptoms, progression, and treatment expectations in a way that a closed-ended questionnaire might miss.

The November 2026 French Guiana study shows why qualitative health research must examine local language, explanatory models, stigma, access barriers, and treatment experiences together. AI qualitative analysis can make that work more manageable by organizing multilingual transcripts, surfacing themes, and supporting transparent comparisons, but human researchers must retain responsibility for interpretation and ethics. Evidano brings these capabilities into one workflow for interviews, documents, and open-ended responses, with features relevant to sensitive public health research. Start your next qualitative project with Try Evidano for free.