From Stories to Evidence: Qualitative Analysis of Stigma Research

According to the November 2, 2026 study "Owning the narrative: Exploring the impact of a creative storytelling intervention on stigma and empowerment among persons affected by leprosy in Pakistan, " participatory storytelling was associated with less self-stigma, greater confidence, and stronger individual empowerment among participants in Karachi, while wider community stigma remained.

• The 2026 PLOS Neglected Tropical Diseases study included 17 total participants, with 16 taking part in photovoice and 14 completing in-depth interviews.
• The study found that the strongest reported changes occurred at the individual level, especially in self-acceptance, confidence, openness, and reduced anticipated stigma.
• The intervention pilot took place in Karachi during 2024, while recruitment occurred from May 30 to June 20, 2025, and interviews were conducted from June 2 to June 25, 2025.
• The study identified gender norms, visible disability, community misconceptions, travel costs, and economic constraints as barriers to broader social change.
• AI-enabled qualitative analysis can help health researchers code interview transcripts, compare themes across gender or disability groups, and retrieve supporting quotations while keeping human interpretation central.

The November 2, 2026 PLOS study examined how participatory storytelling influenced stigma and empowerment among people affected by leprosy in Pakistan. The researchers used a qualitative design combining in-depth interviews and photovoice, then interpreted findings through the Social Ecological Model across individual, interpersonal, community, institutional, and structural levels.

The study recruited adults aged 30 to 65 who had participated in the intervention. According to the study methods, 16 participants contributed to photovoice, 14 completed in-depth interviews, and 13 contributed to both methods, producing a total study group of 17 people. Interviews lasted 60 to 90 minutes and used live Urdu interpretation, while participants typically created up to 10 photographs over approximately three weeks.

The researchers analyzed the material thematically with NVivo 14. The coding process combined deductive codes based on stigma, empowerment, and the Social Ecological Model with inductive codes that emerged from participant accounts. A senior researcher reviewed part of the codebook, and the team resolved coding ambiguities through discussion and consensus.

The study's design is important for AI-enabled qualitative analysis because the evidence included spoken narratives, translated material, photographs, captions, and group discussions. A useful analysis workflow must preserve context across these formats rather than treating frequency counts as a substitute for meaning.

The November 2, 2026 PLOS study suggests that storytelling can support personal empowerment, but storytelling alone may not overcome structural stigma. Participants described movement from shame and concealment toward self-acceptance, confidence, openness, and pride, while community misconceptions and discrimination continued, particularly for people with visible impairments.

One participant described the emotional effect of disclosure by saying, "I have no fear, no shame, not feeling any guilt.... I feel lighter and so relaxed." The PLOS study attributes this quotation to participant P06, a male government worker in his 40s.

The study also found that peer groups created belonging, trust, and mutual recognition. Participant P12 said, "When we were all together, we are doing these activities in a group. It was very good feeling for me that if we are together and unite, so we can do a better work."

For health and policy researchers, the practical lesson is to analyze change at multiple levels. A transcript may contain evidence of individual confidence, peer solidarity, institutional recognition, and persistent community discrimination in the same account. AI can help surface these relationships, but researchers must decide how the evidence fits the study framework and whether a theme reflects a common pattern, a minority experience, or a meaningful exception.

The PLOS authors also caution that the findings should not be treated as causal proof. The study had no baseline or comparator group, included only people who completed the intervention, and relied partly on translated interviews conducted in an institutional setting. These constraints should be carried into any AI-assisted synthesis.

The November 2, 2026 PLOS study shows that participatory storytelling can help people affected by leprosy move from shame and concealment toward confidence, self-acceptance, and peer connection, while broader stigma may persist. AI-enabled qualitative analysis can help health researchers manage multilingual interviews, compare participant segments, trace themes to quotations, and examine evidence across multiple levels of a social intervention. Researchers should use AI to accelerate organization and retrieval while retaining human responsibility for interpretation, ethics, and limitations. Try Evidano for free to analyze qualitative documents and interview evidence more efficiently.