Health researchers need to understand more than what patients know. They also need to identify how language, cultural explanations, stigma, treatment beliefs, and access barriers shape care. Qualitative analysis of health interviews can reveal these factors, but manually reviewing multilingual transcripts, field notes, and subgroup differences takes substantial time. The November 11, 2026 study in PLOS Neglected Tropical Diseases, “Kokobe” and “gwasi”: Perceptions of leprosy in French Guiana, shows why rigorous thematic analysis matters in culturally diverse settings. The study examined interviews with patients and community members in French Guiana and found that local terms, spiritual explanations, misconceptions about transmission, and community support all influenced experiences of leprosy. This article refracts those findings through the lens of AI-enabled qualitative research. The goal is not to replace cultural mediators or clinical judgment, but to help research teams organize evidence, compare segments, preserve participant language, and identify themes that deserve human interpretation.
Key Takeaways
According to the November 11, 2026 PLOS Neglected Tropical Diseases study, qualitative interviews in French Guiana found that local language, spiritual beliefs, stigma, and care access shaped how people understood and managed leprosy.
AI qualitative analysis can help health researchers code large interview collections, compare cultural or geographic segments, and trace how themes connect, while human researchers remain responsible for context, ethics, and interpretation.
- Between April and September 2025, the research team interviewed 20 patients and 37 community members across French Guiana.
- The study included 37 interviews, including 19 patient interviews and 18 community interviews, with interviews conducted in French or through cultural and linguistic mediation.
- The terms “kokobe” and “gwasi” described culturally specific understandings of visible lesions and deformities, but the researchers noted that neither term corresponded exactly to the biomedical category of leprosy.
- Only 2 patients and 1 Western French Guiana community member mentioned bacteria as a cause, while spiritual, familial, environmental, and animal explanations appeared frequently in the interviews.
- The researchers concluded that culturally sensitive communication should clarify person-to-person transmission while taking local terminology, practices, and explanatory models into account.
What the French Guiana Health Interview Study Found
The 2026 PLOS Neglected Tropical Diseases study found that people affected by leprosy and Western French Guiana community members used different explanatory models to describe the disease. The researchers used semi-structured interviews because French Guiana has substantial linguistic diversity, varied literacy levels, and overlapping biomedical and local understandings of illness.
The study found that Maroon community members commonly used “kokobe” for deformities or amputations and “gwasi” for skin lesions. The researchers emphasized that these terms reflected local classifications based on visible manifestations rather than biomedical causation.
The study found that spiritual explanations, including taboo transgression and curses, were common among Maroon and Haitian participants, while bacterial causation was rarely mentioned. One participant described leprosy as “a disease in the blood, caused by bacteria difficult to treat, ” but the researchers reported that only 2 patients and 1 community member referred to bacteria as a cause.
The study also identified confusion between leprosy and diabetes, diagnostic delays, transport difficulties, financial barriers, and stigma. At the same time, most participants described family or community support, and the researchers reported that local plant-based remedies did not appear to undermine adherence to multidrug therapy in this sample.
The study authors summarized the communication challenge directly: “Healthcare professionals should therefore respect cultural representations without discouraging biomedical treatment.” The finding is a research insight, not a clinical instruction; leprosy diagnosis and treatment require qualified healthcare professionals.
Findings Snapshot: AI-Ready Qualitative Research Evidence
| Date or scope | Metric | Value | Implication for qualitative researchers |
|---|---|---|---|
| April to September 2025 | Study fieldwork period | 6 months | Time-bounded collection supports transparent coding and audit trails. |
| 2025 | Patients interviewed | 20 | Patient narratives can be analyzed by cultural background, care pathway, and lived experience. |
| 2025 | Community members interviewed | 37 | Community narratives can be compared with patient accounts without treating the groups as statistically interchangeable. |
| 2025 | Total interviews | 37 | A structured workspace can connect transcripts, field notes, quotations, and codes. |
| 2025 | Patient interviews requiring mediation or a relative | 12 of 19 | Translation and mediation metadata are important for interpreting meaning and potential context loss. |
| 2025 | Community interviews requiring mediation | 17 of 18 | Multilingual qualitative analysis needs explicit attention to language, interpretation, and culturally specific terms. |
| 2015 to 2021 | Reported annual leprosy prevalence in French Guiana | About 1 case per 10, 000 inhabitants | Low-frequency conditions can still require culturally specific community research and communication. |
Implications for AI-Enabled Health Interview Analysis
Why should researchers preserve local terminology in qualitative coding?
Researchers should preserve local terminology because culturally specific words can reveal meanings that biomedical labels obscure. The 2026 PLOS study found that “kokobe” and “gwasi” carried distinct associations with deformity and skin lesions, and one mother understood those terms more readily than French biomedical terms.
AI-assisted coding can retain the original term, create a translated or interpretive label, and connect both to quotations and participant segments. Human researchers must still decide whether a local term describes a disease, a symptom, a social identity, or a broader cultural category.
How can AI help compare patient and community perspectives?
AI can help compare patient and community perspectives by applying a consistent coding framework across separate document groups. The 2026 study deliberately treated patient interviews as accounts of lived experience and care pathways, while community interviews provided collective representations and historical memories.
Cross-segment analysis can show where groups converge, such as recognition of visible skin changes, and where they differ, such as explanations involving spiritual causes or transmission. Researchers should not interpret differences as prevalence estimates when the study design was qualitative and not intended for statistical comparison.
What does multilingual analysis require from a research workflow?
Multilingual analysis requires researchers to record the interview language, interpreter involvement, translation choices, and culturally mediated meanings. The 2026 PLOS study reported interviews in French or with interpretation into participants’ native languages, and 12 of 19 patient interviews required mediation or a relative.
An AI workflow can support transcription, translation, custom dictionaries, and side-by-side review, but translated text should not automatically replace the original transcript or the cultural mediator’s interpretation.
How should health researchers handle sensitive interview data?
Health researchers should minimize exposure of identifiable narratives, restrict access, and avoid treating sensitive transcripts as ordinary public data. The 2026 study did not publicly share interview transcripts because the rarity of leprosy and the small size of some communities could make participants identifiable even after pseudonymization.
AI analysis should therefore operate within a documented governance process covering consent, encryption, access control, retention, and human review. Researchers can review Evidano data security information when evaluating a secure qualitative analysis workflow.
How Evidano Helps With Qualitative Analysis of Health Interviews
Problem: Manual thematic coding slows synthesis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interview transcripts, open-ended survey responses, papers, reports, and other documents.
Evidano can organize interview material into thematic, content, frequency, and cross-segment analyses, helping researchers move from raw transcripts to a structured view of beliefs, barriers, behaviors, and outcomes. Researchers can review Evidano’s qualitative research features to see how the platform supports document analysis.
Problem: Local terms and translated meanings are easy to lose
Solution: Researchers can use transcription and translation workflows with custom dictionaries to preserve terms such as “kokobe” and “gwasi” alongside their research interpretations. Custom terminology is especially relevant when interviews include multiple languages, cultural mediators, or concepts without a direct biomedical equivalent.
The 2026 PLOS study shows why this matters: the researchers found that local terms could communicate meaning more effectively than formal disease names for some participants.
Problem: Segment differences are difficult to track consistently
Solution: Cross-segment analysis can help compare patients and community members, cultural groups, locations, or stages of care using the same code structure. The approach supports questions such as whether stigma, transmission beliefs, treatment confidence, or access barriers appear differently across segments.
The 2026 study cautioned that its groups were not designed for frequency comparisons, so AI-generated patterns should remain descriptive unless a study has an appropriate quantitative design.
Problem: Researchers need to interrogate evidence without rereading every file
Solution: Evidano’s AI chat can help researchers ask targeted questions across uploaded documents and analyses, such as which quotations describe transport barriers or how participants explain treatment effectiveness. Researchers should verify every answer against the underlying quotation and study context.
Evidano states that its data is encrypted and never used to train third-party models, which is relevant for teams handling sensitive health narratives.
FAQ: Qualitative Analysis of Health Interviews
What is qualitative analysis of health interviews?
+
Qualitative analysis of health interviews is a systematic process for identifying themes, meanings, experiences, and relationships in participants’ spoken or written accounts. The 2026 PLOS study used deductive and inductive coding to examine perceptions, explanatory models, stigma, socioeconomic effects, and care pathways.
How many people participated in the French Guiana leprosy study?
+
The French Guiana leprosy study included 20 patients and 37 community members between April and September 2025. The researchers conducted 37 interviews and continued recruitment until thematic saturation was reached.
What did the study find about cultural beliefs and leprosy?
+
The study found that cultural beliefs included spiritual explanations, taboo transgression, familial causes, environmental exposure, and animal-related explanations. The researchers also found that biomedical treatment was generally perceived as effective and that local remedies did not appear to compromise treatment adherence in the study sample.
Can AI replace researchers in qualitative health research?
+
AI cannot replace researchers in qualitative health research because interpretation depends on context, reflexivity, ethics, language, and cultural knowledge. AI can accelerate organization, coding, comparison, retrieval, and visualization, while researchers remain responsible for methodological decisions and final claims.
What is the main limitation of the French Guiana study?
+
The main limitation was the relatively low number of participants within each cultural group, according to the study authors. The authors stated that the findings illustrate a diversity of perspectives rather than provide exhaustive or generalizable characteristics of every cultural group.
Is this article medical advice about leprosy?
+
This article is research-focused and non-diagnostic, not medical advice. The 2026 study discusses perceptions and care pathways, while diagnosis, treatment, transmission guidance, and decisions about traditional remedies require qualified healthcare professionals.
Conclusion and Next Steps
The 2026 French Guiana study demonstrates how qualitative health interviews can reveal the cultural language, beliefs, stigma, support systems, and access barriers behind treatment experiences. AI-enabled qualitative research can make this evidence easier to organize, compare, and retrieve, but culturally informed human interpretation remains essential. A responsible workflow should preserve original language, document mediation, protect participant confidentiality, and connect every theme to supporting quotations. Try Evidano for free to analyze interview transcripts and other qualitative research materials with AI-assisted thematic and cross-segment analysis.
Topics
- qualitative analysis of health interviews
- AI qualitative research
- thematic analysis of interview transcripts
- healthcare interview analysis
- culturally sensitive health research
Keep reading
- Commentary on NewsCulturally Sensitive Qualitative Analysis of Health InterviewsLearn how qualitative analysis of health interviews reveals cultural beliefs, stigma, and care barriers, and how AI helps health researchers act faster.
- Commentary on NewsCultural Insight: AI Qualitative Analysis of LeprosyLearn how AI qualitative analysis of leprosy interviews reveals cultural beliefs, stigma, and care barriers, helping public health researchers act faster.
- Commentary on NewsWhat AI Reveals in Qualitative Analysis of Health InterviewsSee how qualitative analysis of health interviews uncovers cultural beliefs, stigma, and care barriers, and learn how AI can accelerate this work.