Health researchers need qualitative analysis of health interviews to understand why clinical messages, diagnoses, and treatments are interpreted differently across communities. The November 11, 2026 study in PLOS Neglected Tropical Diseases shows how culturally specific language, spiritual explanations, stigma, and access barriers shaped perceptions of leprosy in French Guiana. This article explains what the study found and how AI-enabled qualitative research can help teams organize multilingual interviews, identify themes, compare participant groups, and preserve the context behind sensitive health narratives. The analysis is research-focused and non-diagnostic.
Key Takeaways
According to the PLOS Neglected Tropical Diseases study published on November 11, 2026, qualitative interviews with 20 patients and 37 community members in French Guiana found that local terms, spiritual explanations, treatment beliefs, and stigma influenced how people understood leprosy and navigated care.
The study demonstrates that qualitative analysis of health interviews can reveal communication gaps that standardized surveys may miss, including the different meanings of the terms “kokobe” and “gwasi, ” confusion with diabetes, and the coexistence of traditional remedies with biomedical treatment.
- The PLOS study conducted interviews between April and September 2025 with 20 patients and 37 community members across 37 interviews.
- The PLOS study found that “kokobe” was mainly associated with deformities and amputations, while “gwasi” was mainly associated with skin lesions in Western French Guiana Maroon communities.
- The PLOS study reported that only 2 patients and 1 community member mentioned bacteria as a cause of leprosy, while spiritual explanations were common among Maroon and Haitian participants.
- The PLOS study found that plant-based remedies were widely used, but the reported use of local remedies did not appear to reduce adherence to multidrug therapy.
- The PLOS study reported an annual leprosy prevalence of about 1 case per 10, 000 inhabitants in French Guiana between 2015 and 2021.
How the French Guiana Health Interview Study Worked
The French Guiana study used semi-structured interviews and thematic analysis to examine illness perceptions, explanatory models, stigma, socioeconomic effects, and care-seeking pathways. The research team recruited adults with active or cured leprosy from health centres and recruited community members through a snowball approach in Western French Guiana villages connected to the former Acarouany leprosarium.
The PLOS study used purposive sampling for patient interviews and continued recruitment until thematic saturation was reached. Researchers conducted interviews in French or with cultural mediation into participants’ native languages, manually transcribed audio-recorded patient interviews, and coded quotations in an Excel database.
The PLOS study combined deductive coding based on the interview guide with inductive coding for emerging cultural meanings. Regular discussions among dermatologists, an anthropologist, and a Maroon cultural health mediator supported iterative refinement of themes and interpretation.
The study’s approach matters for AI-enabled qualitative research because the dataset combined multiple languages, individual and group interviews, mediated conversations, sensitive personal narratives, and culturally specific terms. The PLOS authors also stated that transcripts could not be publicly shared because participants might be identifiable despite pseudonymization.
Qualitative Analysis of Health Interviews: Findings Snapshot
| Date or period | Metric | Value | Implication for researchers |
|---|---|---|---|
| April to September 2025 | Patient participants | 20 patients | The PLOS study used patient narratives to examine lived experience, explanatory models, and care pathways. |
| April to September 2025 | Community participants | 37 community members | The PLOS study used community narratives to examine collective representations and historical memories. |
| April to September 2025 | Total interviews | 37 interviews | The PLOS study combined individual, group, and culturally mediated interviews. |
| 2015 to 2021 | Annual leprosy prevalence in French Guiana | About 1 case per 10, 000 inhabitants | The PLOS study described leprosy as a persistent public health issue rather than only a historical condition. |
| During the 2025 study | Patient interviews requiring mediation or a relative | 12 of 19 patient interviews | The PLOS study shows why language and cultural context must remain visible during coding. |
| During the 2025 study | Community interviews requiring mediation | 17 of 18 community interviews | The PLOS study shows why translated or mediated qualitative data requires careful contextual interpretation. |
What the Findings Mean for Health Researchers
Why should researchers code local terms instead of treating them as simple translations?
Researchers should code local terms as culturally meaningful concepts because the PLOS study found that “kokobe” and “gwasi” did not map exactly onto the biomedical category of leprosy.
The study linked “gwasi” mainly to skin lesions and “kokobe” mainly to deformities or amputations, although participants also described a continuum between the terms. The PLOS authors quoted one community member saying, “It starts with spots... If you don't treat it, your fingers can bend.”
How did cultural explanations affect leprosy communication?
Cultural explanations affected communication because the PLOS study found that bacterial causation was rarely mentioned, while participants referred to inherited taboos, curses, food, animals, environmental exposure, and family transmission.
The study quoted a participant who said, “It's a family disease that is transmitted through the father's blood, ” illustrating why a health communication strategy must address the participant’s explanatory model rather than only repeat biomedical terminology.
What did the study reveal about stigma and support?
The PLOS study found that stigma and support coexisted: some patients described discrimination, fear, self-isolation, or barriers in public services, while many also reported strong family and community support.
A Western French Guiana community member told researchers, “They are not separated from the community... we live together normally, we talk and eat together.” Qualitative analysis preserved this contrast more clearly than a single stigma score would.
How can health teams turn these findings into action?
Health teams can use these findings to design culturally sensitive communication that explains person-to-person transmission, recognizes local terminology, clarifies treatment effectiveness, and discusses traditional practices without dismissing participants’ beliefs.
The PLOS authors recommended that healthcare professionals become familiar with local terms and that any integration of local remedies should consider safety, possible interactions with multidrug therapy, and timely referral to biomedical services.
How Evidano Supports AI-Enabled Qualitative Research
Problem: Multilingual interviews make consistent coding difficult
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Researchers can use Evidano’s transcription and translation capabilities to organize interview material across languages, while custom dictionaries can preserve terms such as “kokobe, ” “gwasi, ” and community-specific health vocabulary. Researchers should validate translations and interpretations with cultural experts because AI cannot replace local knowledge or reflexive judgment.
Teams can learn more about the relevant Evidano features for qualitative research.
Problem: Large interview sets slow thematic synthesis
Evidano’s thematic, content, frequency, and cross-segment analyses can help research teams move from raw transcripts to structured patterns across participant groups.
For a study modeled on the French Guiana research, analysts could compare patients and community members, cultural backgrounds, interview settings, or reported care pathways. Researchers can then review supporting quotations before finalizing interpretations.
Problem: Sensitive health narratives require controlled access
Evidano encrypts data and does not use customer data to train third-party models, which is relevant for research involving rare diseases, stigma, and potentially identifiable narratives.
The French Guiana study did not publicly release interview transcripts because contextual details could identify participants. Researchers using AI analysis should therefore apply informed consent, data minimization, role-based access, and human review before exporting or sharing results. More information is available on Evidano data security.
Problem: Researchers need to interrogate findings without losing the source context
Evidano’s AI chat over documents and analyses can help researchers ask follow-up questions about themes, segments, quotations, and contradictions within an approved research workspace.
A researcher could ask which participants associated leprosy with family lineage, how often treatment adherence appeared alongside local remedy use, or which quotations described discrimination. Researchers should treat AI-generated answers as an analysis aid and verify every important conclusion against the original transcript.
FAQ: Qualitative Analysis of Health Interviews
What is qualitative analysis of health interviews?
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Qualitative analysis of health interviews is a systematic process for identifying meanings, experiences, explanatory models, and recurring themes in participants’ spoken or written accounts.
The PLOS French Guiana study used deductive and inductive thematic analysis to connect interview quotations with themes including cultural knowledge, care pathways, stigma, and treatment practices.
How many people participated in the French Guiana leprosy study?
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The French Guiana study included 20 patients and 37 community members between April and September 2025.
The PLOS authors reported 37 interviews in total, including 19 patient interviews and 18 community interviews.
What did the study find about “kokobe” and “gwasi”?
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The study found that “gwasi” mainly referred to skin lesions and “kokobe” mainly referred to deformities or amputations in Western French Guiana Maroon communities.
The PLOS authors emphasized that the terms represented local classifications based on visible manifestations and did not correspond exactly to the biomedical category of leprosy.
Can AI replace researchers in qualitative health research?
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AI can accelerate transcription, organization, coding, comparison, and retrieval, but AI cannot replace researchers’ cultural expertise, ethical judgment, reflexivity, or responsibility for interpretation.
The PLOS study relied on cultural mediation, multidisciplinary review, and iterative discussions, which are essential safeguards when analyzing multilingual and sensitive health narratives.
How should researchers protect sensitive interview transcripts?
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Researchers should protect sensitive interview transcripts through informed consent, pseudonymization, encryption, restricted access, data minimization, and careful review before sharing results.
The PLOS study kept transcripts securely at the French Guiana University Hospital because participants could potentially be identified from contextual information even after pseudonymization.
Conclusion and Next Steps
The November 11, 2026 PLOS study shows that qualitative analysis of health interviews can uncover how language, cultural beliefs, stigma, treatment experiences, and community support shape healthcare journeys.
AI-enabled qualitative research can help teams process complex interview collections faster, compare segments consistently, and retrieve the quotations needed to support transparent interpretation. Human researchers must still validate translations, protect participant privacy, and interpret culturally specific meanings responsibly.
Researchers working with multilingual interviews, open-ended health data, or sensitive patient narratives can try Evidano for free.
Topics
- qualitative analysis of health interviews
- AI qualitative research
- thematic analysis of health interviews
- cultural health research
- leprosy perceptions
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