Public health researchers need to understand more than symptoms and treatment outcomes. They also need to identify the cultural beliefs, language, stigma, and care experiences that influence whether people disclose illness and seek treatment. Qualitative analysis of leprosy perceptions is especially important in multicultural settings, where local terms and explanatory models may differ from biomedical language. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents while preserving a clear audit trail from source material to themes. A November 2026 study in French Guiana shows why this matters: interviews revealed that local concepts such as kokobe and gwasi, spiritual explanations, confusion with diabetes, and transport barriers shaped how participants understood leprosy and navigated care. This article summarizes the study and explains how AI-enabled thematic analysis can help health researchers organize multilingual, sensitive qualitative data without replacing cultural expertise or ethical judgment.
Key Takeaways
According to the November 11, 2026 study in PLoS Neglected Tropical Diseases, perceptions of leprosy in French Guiana were shaped by local terminology, spiritual and familial explanations, stigma, and practical barriers to care.
The study shows that qualitative analysis can reveal why biomedical information does not always translate into shared understanding, while AI-assisted analysis can help researchers compare themes across languages, cultural groups, and care experiences.
- The study conducted between April and September 2025 included 20 patients and 37 community members across 37 interviews.
- The researchers found that Maroon and Haitian participants commonly emphasized spiritual explanations, while only 2 patients and 1 community member referred to bacteria as a cause of leprosy.
- The local terms kokobe and gwasi referred mainly to deformities and skin lesions, but the researchers stated that the terms did not correspond exactly to the biomedical category of leprosy.
- The study reported that plant-based remedies were widely used, but local remedies did not appear to reduce adherence to multidrug therapy in the study sample.
- The authors concluded on November 11, 2026, that culturally sensitive communication should clarify person-to-person transmission while taking local terminology and practices into account.
What the 2026 French Guiana Study Found
The November 2026 PLoS Neglected Tropical Diseases study used semi-structured interviews and thematic analysis to examine how people affected by leprosy and Maroon community members understood the disease.
The study recruited 20 adult patients from health centres across French Guiana and 37 community members from western French Guiana villages associated with the former Acarouany leprosarium. The researchers continued recruitment until thematic saturation, meaning that additional interviews produced no substantially new themes or explanatory models.
The researchers conducted interviews in French or through cultural mediation in participants' native languages. The patient interviews were audio-recorded and transcribed verbatim, while community interviews often relied on detailed field notes because linguistic mediation was usually required.
The study team manually transcribed the interviews, coded the material in an Excel database, and combined deductive coding from the interview guide with inductive coding for emergent cultural meanings. Regular discussions among dermatologists, an anthropologist, and a Maroon cultural health mediator supported interpretation and reflexivity.
The authors wrote that local terms and explanations affected disclosure, diagnosis, and care pathways. One WFG community member described the progression of visible symptoms: "It starts with spots... If you don't treat it, your fingers can bend."
The study also documented uncertainty about transmission. A community member stated, "We don't know how it is acquired, " while the authors noted that bacterial causation was rarely mentioned and that several participants associated leprosy with food, animals, housing, soil, family lineage, curses, or inherited taboos.
The study was not designed to generalize beliefs across every cultural group in French Guiana. The authors identified the small number of participants within each cultural group as a limitation and described the findings as a diversity of perspectives rather than exhaustive cultural characteristics.
Findings Snapshot: Numbers Behind the Qualitative Analysis
| Date or period | Metric | Value | Research implication |
|---|---|---|---|
| April to September 2025 | Patient participants | 20 | The study captured lived experiences, explanatory models, and care pathways among adults with leprosy. |
| April to September 2025 | Western French Guiana community participants | 37 | The community sample added collective narratives and historical memories linked to leprosy. |
| April to September 2025 | Total interviews | 37 | The researchers analyzed individual, group, and mediated conversations across patient and community settings. |
| April to September 2025 | Patient interviews requiring mediation or a relative | 12 of 19 | The finding demonstrates why multilingual and culturally mediated qualitative data requires careful interpretation. |
| April to September 2025 | Community interviews requiring mediation | 17 of 18 | The finding shows that language and cultural mediation were central to data collection. |
| 2015 to 2021 | Annual leprosy prevalence in French Guiana | About 1 case per 10, 000 inhabitants | The background evidence cited by the study shows that leprosy remained an endemic public health issue. |
| November 11, 2026 | Participants referring to bacteria as a cause | 2 patients and 1 community member | The small count illustrates the gap between biomedical explanations and locally held models of illness. |
Implications for Public Health and Qualitative Researchers
Why do local words matter in health research?
Local words matter because the November 2026 study found that kokobe and gwasi described visible manifestations and disability rather than mapping precisely onto the biomedical category of leprosy.
Researchers who search only for the clinical term leprosy could miss relevant accounts of symptoms, stigma, and care. A robust coding framework should include local terminology, translations, related conditions, and participant language.
What does the study imply for culturally sensitive communication?
Culturally sensitive communication should acknowledge local explanatory models while clearly explaining biomedical transmission and treatment, according to the November 2026 study authors.
The study found that spiritual beliefs and plant-based remedies could coexist with confidence in biomedical treatment. Health teams therefore need to understand participants' practices without assuming that cultural beliefs automatically cause non-adherence.
How can AI improve qualitative analysis without replacing researchers?
AI can improve qualitative analysis by accelerating transcription, translation, coding, theme comparison, and retrieval across large collections, while researchers remain responsible for interpretation, reflexivity, and ethical decisions.
The French Guiana study demonstrates why human oversight is essential: the researchers used cultural mediation, multidisciplinary review, and contextual interpretation to distinguish local categories from biomedical ones.
Sensitive health data requires strict governance. The November 2026 study did not publicly share interview transcripts because rare disease narratives and community context could make participants identifiable even after pseudonymization.
How Evidano Supports AI-Enabled Thematic Analysis
Problem: Manual transcription slows health research
Evidano is an AI-powered qualitative data analysis platform that helps researchers transcribe and analyze sensitive interviews, documents, and open-ended survey responses.
For studies like the French Guiana research, Evidano's speech-to-text workflow can help convert recorded interviews into searchable transcripts, while custom dictionaries can preserve local terms such as kokobe and gwasi. Researchers can learn more about Evidano's speech-to-text capabilities.
Problem: Multilingual interviews can fragment the evidence
Evidano can translate qualitative materials with custom dictionaries, helping research teams compare concepts across French, Haitian Creole, Maroon languages, and other study languages while retaining defined terminology.
Researchers should validate translated passages against the original audio, transcript, or field notes because culturally specific meanings cannot always be translated literally.
Problem: Important themes are distributed across interviews
Evidano's thematic, content, frequency, and cross-segment analyses can help researchers organize recurring topics such as transmission beliefs, stigma, treatment adherence, family support, and access barriers.
Researchers can use hierarchical codes and subcodes to separate broad themes from culturally specific expressions, then compare findings by participant group, location, language, or care experience.
Problem: Researchers need to interrogate evidence efficiently
Evidano's AI chat over documents and analyses can help researchers retrieve supporting excerpts and ask targeted questions across a qualitative dataset.
Researchers should treat AI-generated summaries as analytical aids, not final conclusions. The source study's approach shows the importance of returning to quotations, field context, reflexive discussion, and participant confidentiality.
Problem: Sensitive health narratives require data protection
Evidano encrypts data and does not use customer data to train third-party models, which is relevant when analyzing rare disease interviews and identifiable community narratives.
Research teams should still apply informed consent, access controls, minimization, pseudonymization, and institutional ethics requirements before uploading qualitative data. Teams can review Evidano's data security information.
FAQ: Qualitative Analysis of Leprosy Perceptions
What did the 2026 qualitative study find about leprosy perceptions in French Guiana?
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The November 2026 study found that leprosy perceptions were shaped by local terms, spiritual and familial explanations, confusion with diabetes, stigma, and barriers to diagnosis and follow-up.
The study included 20 patients and 37 community members across 37 interviews conducted between April and September 2025.
What do kokobe and gwasi mean in the study?
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According to the November 2026 study, gwasi was mainly associated with skin lesions, while kokobe was mainly associated with deformities or amputation of the extremities.
The authors cautioned that neither term corresponded exactly to the biomedical category of leprosy, because the terms reflected local classifications based largely on visible manifestations.
How were the French Guiana interviews analyzed?
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The researchers manually transcribed patient interviews, coded quotations in an Excel database, and used deductive and inductive thematic analysis, according to the November 2026 study.
The researchers also used cultural mediation, multidisciplinary review, and iterative discussions to refine themes and reduce interpretive bias.
Can AI perform culturally sensitive qualitative analysis by itself?
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AI cannot perform culturally sensitive qualitative analysis by itself because interpretation depends on language, history, context, reflexivity, and ethical judgment.
AI can support transcription, translation, coding, retrieval, and cross-segment comparison, but researchers must validate outputs against source material and consult cultural experts where appropriate.
How can researchers use AI to analyze sensitive health interviews responsibly?
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Researchers can use AI responsibly by obtaining informed consent, limiting access, protecting personally identifiable information, validating automated outputs, and documenting analytical decisions.
The November 2026 study illustrates the importance of confidentiality because the authors withheld transcripts from public sharing due to the risk that rare disease narratives and contextual details could identify participants.
Conclusion and Next Steps for AI-Enabled Health Research
The November 2026 French Guiana study shows that qualitative analysis of leprosy perceptions can uncover barriers and sources of support that clinical measures alone may miss.
The study's 57 participants revealed how terminology, spiritual beliefs, stigma, treatment experiences, family support, and transport barriers influenced understanding and care pathways.
AI-enabled qualitative research can help teams process multilingual interviews and compare themes more efficiently, but responsible interpretation still depends on human researchers, cultural expertise, and strong data governance.
Health researchers who want to accelerate transcription, thematic coding, cross-segment analysis, and evidence retrieval can Try Evidano for free.
Topics
- qualitative analysis of leprosy perceptions
- AI qualitative research in public health
- thematic analysis of health interviews
- culturally sensitive health research
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