Health researchers often need to identify cultural beliefs, stigma, language differences, and care barriers across dozens of interviews. Qualitative analysis of health interviews can reveal these patterns, but manual transcription, translation, coding, and cross-segment comparison can slow decisions and make culturally specific language easier to miss. The 2026 PLOS Neglected Tropical Diseases study on leprosy in French Guiana provides a useful example: researchers combined semi-structured interviews, cultural mediation, and thematic analysis to understand how local terms, spiritual explanations, and treatment experiences shaped care pathways. This article explains what the study found and how AI-enabled qualitative research can support similar work without replacing human interpretation.
Key Takeaways
According to the November 11, 2026 study, “Kokobe” and “gwasi”: Perceptions of leprosy in French Guiana, culturally grounded qualitative analysis showed that local disease terms, spiritual explanations, stigma, and treatment beliefs influenced how people understood leprosy and navigated care.
The study demonstrates that qualitative analysis of health interviews is most useful when researchers preserve participants’ own language, compare patient and community perspectives, and connect themes to practical communication and care decisions.
- Between April and September 2025, the PLOS study interviewed 20 patients and 37 community members in French Guiana across 37 interviews.
- The PLOS researchers found that the Maroon terms “kokobe” and “gwasi” referred primarily to deformities and skin patches, rather than matching the biomedical category of leprosy exactly.
- The PLOS study reported that only 2 patients and 1 Western French Guiana community member mentioned bacteria as a cause of leprosy, while spiritual and familial explanations were common among Maroon and Haitian participants.
- The PLOS researchers found that plant-based remedies were widely used, but the remedies did not appear to reduce adherence to multidrug therapy in the study sample.
- The PLOS authors concluded that culturally sensitive communication should clarify person-to-person transmission while taking local terminology, practices, and explanatory models seriously.
What the French Guiana Health Interview Study Found
The PLOS study found that health beliefs were shaped by language, cultural background, community memory, and previous experiences with healthcare. Researchers interviewed 20 patients across French Guiana health centres and 37 community members in Western French Guiana villages between April and September 2025.
The PLOS research team used semi-structured interviews because French Guiana has substantial linguistic diversity, varied literacy levels, and overlapping biomedical and local representations of illness. Patient interviews were conducted in French or with interpretation, while community interviews often relied on cultural mediation and detailed field notes.
The PLOS authors used deductive and inductive thematic analysis. The researchers first organized material around interview-guide domains, then added emergent themes and culturally specific meanings. The research team manually transcribed interviews and organized codes, quotations, participant identifiers, and communities of origin in an Excel database.
The study’s findings show why literal keyword matching is insufficient for health research. One community member explained, “It starts with spots... If you don't treat it, your fingers can bend, ” linking the local understanding of skin signs to later disability. Another participant said, “We don't know how it is acquired, ” illustrating a knowledge gap about transmission.
The study also identified a communication challenge around local terminology. The PLOS authors reported that one child’s mother did not recognize the French terms for leprosy but understood the locally used terms “gwasi” and “kokobe.”
Findings Snapshot: Qualitative Health Interview Analysis
| Date or study measure | Evidence reported by the PLOS study | Implication for qualitative researchers |
|---|---|---|
| April to September 2025 | The study collected qualitative data during this six-month period. | Time-bounded fieldwork helps researchers connect themes to a defined social and healthcare context. |
| 20 patients | The researchers interviewed 20 people affected by leprosy across French Guiana health centres. | Patient interviews can reveal lived experience, diagnostic journeys, stigma, and treatment perceptions. |
| 37 community members | The researchers interviewed 37 community members in Western French Guiana villages. | Community interviews can add historical memory and collective representations that patient interviews may not capture. |
| 37 interviews | The study included 19 patient interviews and 18 community interviews. | A transparent interview structure helps analysts distinguish individual experience from community narratives. |
| 5 languages in patient interviews | The PLOS study reported that 12 patient interviews required mediation or a relative and involved five different languages. | Translation and cultural mediation should be treated as part of the research context, not as a minor transcription detail. |
| 58.2% versus 0.53% | The study cited peripheral neuropathy estimates showing diabetes at 58.2% and leprosy at 0.53% in French Guiana. | Confusion with more familiar conditions can affect diagnosis narratives and should be coded as a distinct theme. |
Implications for Public Health and UX Research Teams
Why should health researchers analyze local terms separately from medical categories?
Health researchers should analyze local terms separately because the PLOS study found that “kokobe” and “gwasi” did not map exactly to the biomedical category of leprosy.
A local term may describe a visible symptom, a disability, a social identity, or a culturally specific cause. Coding those meanings separately can help researchers design clearer health messages and avoid treating translation as a simple word-for-word exercise.
How can qualitative analysis identify barriers to diagnosis and care?
Qualitative analysis can identify care barriers by linking interview statements about symptoms, transport, cost, misdiagnosis, disclosure, and treatment to a single care-pathway framework.
The PLOS study reported prolonged and fragmented diagnostic journeys, transport problems, financial barriers, and occasional treatment interruptions. Researchers can use cross-segment analysis to compare these barriers by language, cultural background, location, or patient status.
What should researchers do with sensitive health interview transcripts?
Researchers should protect sensitive health transcripts through pseudonymization, restricted access, and careful decisions about data sharing. The PLOS authors did not make the interview transcripts publicly available because participants could potentially be identified through detailed narratives, rare disease status, and small community settings.
AI-assisted analysis should therefore support ethical research governance rather than encourage unrestricted uploading of identifiable health data. Researchers should remove or redact personally identifying information before analysis and verify that the selected platform has appropriate security controls.
What does this study teach teams designing culturally sensitive research?
The PLOS study shows that culturally sensitive research must combine local language, community mediation, reflexive analysis, and biomedical context.
The research team included dermatologists, a medical anthropologist, and a Maroon cultural health mediator. That multidisciplinary structure helped the authors interpret local expressions and distinguish cultural explanations from clinical categories.
How Evidano Supports AI Qualitative Research
Problem: Manual transcription slows analysis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interview transcripts, open-ended surveys, and health documents.
For multilingual or mediated studies like the French Guiana research, Evidano can support transcription and translation workflows before researchers conduct thematic, content, frequency, and cross-segment analyses. Researchers can learn more about Evidano speech-to-text and transcription.
Problem: Culturally specific expressions can disappear during coding
Evidano can help researchers preserve recurring local terms, participant language, and contextual quotations while organizing themes into codes and subcodes.
Researchers can use hierarchical code structures to separate visible symptoms, local disease categories, spiritual explanations, biomedical explanations, stigma, treatment adherence, and access barriers. Human researchers should still review translations, code definitions, and culturally sensitive interpretations.
Problem: Differences between participant groups are difficult to compare
Evidano supports cross-segment analysis so researchers can compare themes across patient groups, communities, languages, locations, or demographic segments.
In a study modeled on the PLOS research, cross-segment analysis could compare patient narratives with community narratives, or compare themes associated with Maroon, Haitian, Brazilian, and other participant backgrounds. Researchers can also use visualizations such as co-occurrence networks and hierarchical codes to inspect relationships between stigma, disclosure, beliefs, and care pathways.
Problem: Researchers need defensible answers from large document sets
Evidano provides AI chat over uploaded documents and analyses, allowing research teams to ask focused questions about themes, quotations, segments, and evidence.
Researchers should use AI-generated outputs as an analysis aid, then check important interpretations against the original transcript, field notes, translation context, and research team discussion. Evidano states that data is encrypted and never used to train third-party models; teams can review the platform’s data security information before uploading sensitive research materials.
FAQ: Qualitative Analysis of Health Interviews
What is qualitative analysis of health interviews?
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Qualitative analysis of health interviews is a method for identifying meanings, experiences, beliefs, behaviors, and social patterns in spoken or written health narratives.
The PLOS French Guiana study used thematic analysis to examine perceptions of leprosy, care pathways, stigma, explanatory models, and treatment practices across patient and community interviews.
How many people participated in the French Guiana leprosy study?
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The PLOS study included 20 patients and 37 community members between April and September 2025.
The researchers conducted 37 interviews in total, including 19 patient interviews and 18 community interviews.
What were “kokobe” and “gwasi” in the study?
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According to the PLOS study, “kokobe” and “gwasi” were local terms used in Maroon communities to describe manifestations associated with leprosy.
The study reported that “gwasi” was mainly associated with skin lesions, while “kokobe” was more closely associated with deformities or amputations. The authors emphasized that neither term exactly matched the biomedical category of leprosy.
Can AI replace human researchers in thematic analysis?
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AI can accelerate transcription, translation, coding, theme discovery, and cross-segment comparison, but AI should not replace human researchers’ cultural interpretation, reflexivity, or ethical judgment.
The PLOS study relied on multidisciplinary discussion and cultural mediation to refine interpretation. Those human safeguards remain important when AI analyzes multilingual, sensitive, or culturally specific health data.
How can researchers protect sensitive interview data during AI analysis?
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Researchers can protect sensitive interview data by removing direct identifiers, limiting access, reviewing vendor security practices, and retaining human oversight over outputs.
The PLOS authors withheld transcripts from public release because rare disease narratives and small communities could make participants identifiable even after pseudonymization. Research teams should apply comparable caution when using AI analysis tools.
Conclusion and Next Steps
The November 11, 2026 PLOS study shows that health interview analysis can reveal how local language, spiritual beliefs, stigma, treatment experiences, and access barriers shape care. The study’s 20 patient and 37 community interviews also demonstrate the value of combining thematic analysis with cultural mediation and multidisciplinary review.
AI-enabled qualitative research can make this workflow more manageable by helping teams transcribe, translate, code, compare segments, and retrieve supporting quotations across large collections of interviews. Human researchers remain responsible for validating interpretations, protecting participants, and preserving cultural context.
Evidano helps research teams turn sensitive qualitative data into structured, reviewable evidence while keeping analysis connected to the original documents. Try Evidano for free and explore a faster workflow for qualitative health interview analysis.
Topics
- qualitative analysis of health interviews
- AI qualitative research
- thematic analysis of interview transcripts
- culturally sensitive health research
- qualitative health data analysis
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