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From Stories to Evidence: Qualitative Analysis of Health Interviews

Evidano8 min read

Qualitative analysis of health interviews helps researchers understand how people experience illness, stigma, care, and social change. The challenge is turning sensitive interviews, translated transcripts, and participatory materials into defensible themes without losing the participants’ own words. A November 2026 study in PLOS Neglected Tropical Diseases provides a useful example for public health researchers, programme evaluators, and qualitative teams. The study examined a participatory storytelling intervention for people affected by leprosy in Karachi, Pakistan, using in-depth interviews and photovoice. This article summarizes what the researchers found and explains how AI-enabled qualitative research can support similar workflows. The focus is research analysis, not diagnosis or clinical advice.

Key Takeaways

According to the November 2, 2026 PLOS Neglected Tropical Diseases study, participatory storytelling was associated with a shift from shame and concealment toward self-acceptance, confidence, and openness among people affected by leprosy in Karachi, Pakistan. The study found that individual empowerment was stronger than broader community change, because misconceptions, gender norms, visible disability, and economic barriers continued to reinforce stigma.

  • The study included 17 participants in total, with 16 taking part in photovoice and 14 completing in-depth interviews during data collection in June 2025.
  • All participants reported greater confidence after the intervention, while the strongest reported changes involved reduced self-stigma and anticipated stigma.
  • The 2024 storytelling pilot combined peer support, creative activities, health education, advocacy, and contact-based engagement through the Marie Adelaide Leprosy Centre in Karachi.
  • The researchers found that peer solidarity and institutional recognition supported empowerment, but episodic contact did not produce consistent community-level change by June 2025.
  • The study recommends sustained community contact, formal peer roles, inclusive outreach to women and people with visible impairments, and links to socioeconomic opportunities.

What the Health Interview Study Examined

The November 2026 PLOS study examined how participatory storytelling influenced stigma and empowerment among people affected by leprosy in Pakistan. The research followed a 2024 to 2025 pilot at the Marie Adelaide Leprosy Centre in Karachi, where participants joined peer support groups, creative sessions, health education, advocacy activities, and public storytelling.

The researchers used two qualitative methods: in-depth interviews and photovoice. Sixteen participants created and discussed photographs, 14 participants completed interviews, and 13 participants took part in both activities. Interviews lasted 60 to 90 minutes and were conducted in person between June 2 and June 25, 2025, with live Urdu interpretation.

The researchers analyzed the material thematically using NVivo 14 and interpreted the findings through the Social Ecological Model. The analysis considered individual, interpersonal, community, institutional, and structural influences rather than treating stigma as an individual attitude alone.

The researchers reported that participants moved from shame and concealment toward self-acceptance, confidence, and openness. One participant described the emotional effect of disclosure by saying, "I have no fear, no shame, not feeling any guilt. I’m feeling like... I feel lighter and so relaxed."

The PLOS study also identified important limits. Community misconceptions remained, women faced restrictions related to household permission and mixed-gender settings, participants with visible impairments continued to experience discrimination, and travel costs and time pressures affected participation.

Qualitative Analysis of Health Interviews: Findings Snapshot

Date or periodMetricValueResearch implication
2024Storytelling pilotOne-year pilotThe intervention combined storytelling with peer support, health education, advocacy, and contact activities.
May 30 to June 20, 2025Recruitment period22 daysPurposive sampling sought variation in gender, age, disability, diagnosis history, and social circumstances.
June 2025Total study participants17The findings offer detailed contextual insight but should not be treated as population estimates.
June 2025Photovoice participants16Visual storytelling added participant-generated images, captions, and group reflection to the interview data.
June 2025In-depth interview participants14Interview data supported detailed exploration of identity, stigma, empowerment, and intervention experiences.
June 2025Interview duration60 to 90 minutesLong conversational interviews generated rich narrative data requiring systematic coding and comparison.
November 2, 2026Women represented in the sample4 of 17 participantsThe authors identified limited female representation as a constraint on gender-specific interpretation.

Implications for Public Health and Qualitative Research Teams

The November 2026 PLOS study suggests that qualitative health research should separate changes in self-stigma from changes in public stigma. Participants described stronger confidence, self-acceptance, communication skills, and peer belonging, but these individual and interpersonal gains did not consistently overcome community misconceptions or structural barriers.

For public health programme teams, the study supports a multi-level evaluation plan. Researchers should examine what changes for participants, how peer relationships influence those changes, whether institutions create credible roles for people with lived experience, and whether community contact is repeated and broad enough to shift social norms.

For qualitative analysts, the study demonstrates the value of combining interview transcripts with photovoice materials. The PLOS researchers used photographs, captions, group discussions, and interviews to compare personal accounts with participant-created visual metaphors. One participant summarized the shared experience as follows: "Every face is different, but the colour of blood is the same. The pictures are different, but the story is the same."

The study also highlights why demographic and contextual segmentation matters. The authors compared experiences across gender, visible disability, employment, household responsibilities, and social setting. A single overall theme such as empowerment would have concealed the different barriers faced by women, people with Grade 2 disability, and participants managing work or caregiving responsibilities.

Researchers should treat AI as an aid to organization, comparison, and retrieval rather than as a substitute for ethical judgment. The PLOS study used participant consent, anonymization, translation checks, reflexivity, and researcher review, and those safeguards remain necessary when AI is introduced into sensitive qualitative workflows.

How Evidano Helps With AI-Enabled Health Interview Analysis

Problem: Interview and photovoice data are difficult to organize

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended survey responses, papers, reports, and other documents.

Evidano can bring interview transcripts, translated materials, field notes, and research documents into one analysis workspace. Researchers can then use thematic, content, frequency, and cross-segment analyses to compare recurring concepts such as stigma, confidence, disclosure, peer support, and community barriers.

Researchers can learn more about Evidano’s relevant qualitative research analysis features.

Problem: Multilingual interviews can introduce meaning loss

The November 2026 PLOS study used live Urdu interpretation and repeated meaning checks, showing why language handling is central to health interview analysis.

Evidano supports transcription and translation workflows with custom dictionaries, which can help research teams preserve recurring clinical, cultural, and programme-specific terminology before thematic coding.

Problem: Researchers need to compare themes across participant groups

The PLOS authors examined differences related to gender, visible disability, employment, and social circumstances, and Evidano can support comparable cross-segment analysis across demographic or programme-defined groups.

Researchers can use hierarchical codes and subcodes to distinguish broad themes such as empowerment from more specific patterns such as self-acceptance, communication skills, public visibility, or barriers to participation.

Problem: Analysts need an auditable way to interrogate a large document set

Evidano’s AI chat over documents and analyses allows researchers to ask focused questions about their source material, such as which participants described disclosure, where community stigma persisted, or how often transport barriers appeared.

AI-generated findings still require researcher review, especially when data involves illness, stigma, translation, or vulnerable participants. Evidano encrypts data and does not use customer data to train third-party models, as described in its data security information.

FAQ: Qualitative Analysis of Health Interviews

What did the 2026 leprosy storytelling study find?

The November 2026 PLOS study found that participatory storytelling was associated with greater self-acceptance, confidence, openness, peer solidarity, and individual empowerment among participants affected by leprosy in Karachi.

The study also found that community misconceptions, gender norms, visible disability, and economic barriers limited broader social change.

How many people took part in the qualitative study?

The study included 17 participants in total, with 16 participating in photovoice and 14 completing in-depth interviews during June 2025.

Thirteen participants took part in both photovoice and in-depth interviews, allowing the researchers to compare narrative and visual data.

What is photovoice in qualitative health research?

Photovoice is a participatory qualitative method in which participants create or select photographs and explain the meaning of those images through individual or group discussion.

In the November 2026 PLOS study, participants typically took up to 10 photographs over approximately three weeks, then selected images for reflection and thematic discussion.

Can AI analyze health interview transcripts?

AI can help organize, code, summarize, compare, and retrieve patterns from health interview transcripts, but researchers remain responsible for interpretation, ethics, confidentiality, and validation.

Evidano is designed for qualitative workflows that include thematic analysis, cross-segment comparison, document chat, transcription, and translation, while keeping human review central to sensitive research.

What should researchers check before using AI for sensitive interviews?

Researchers should check consent, privacy protection, data security, translation accuracy, sampling context, model limitations, and whether AI findings are supported by the original participant accounts.

The November 2026 PLOS study used anonymization, secure storage, consent procedures, translator checks, reflexivity, and researcher review, which provide useful safeguards for AI-assisted qualitative analysis.

Conclusion and Next Steps

The November 2026 PLOS study shows how qualitative analysis of health interviews can reveal both the personal value and structural limits of participatory storytelling. The study’s 17-person sample documented stronger self-acceptance and empowerment, while also showing why gender-responsive design, repeated community contact, disability inclusion, and socioeconomic support matter.

AI-enabled qualitative research can help teams manage multilingual interviews, visual and narrative materials, thematic coding, and cross-segment comparisons more consistently. Researchers should use AI to accelerate evidence organization and questioning, while preserving participant context, ethical safeguards, and human interpretation.

Teams analyzing interviews, open-ended surveys, or health programme documents can try Evidano for free and explore an AI-assisted workflow for qualitative research.

Topics

  • qualitative analysis of health interviews
  • AI qualitative research analysis
  • health interview thematic analysis
  • leprosy stigma research
  • participatory storytelling research

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