From Stories to Evidence: AI Qualitative Analysis

According to the PLOS Neglected Tropical Diseases study published on November 2, 2026, participatory storytelling in Karachi was associated with greater self-acceptance, confidence, peer solidarity, and openness among people affected by leprosy, although community stigma and structural barriers continued.

• The 2026 qualitative study included 17 total participants, with 16 people taking part in photovoice and 14 completing in-depth interviews.
• The study collected data during a four-week period in June 2025 after a storytelling pilot launched in Karachi in 2024.
• The strongest reported effects occurred at the individual level, where participants described movement from shame and concealment toward self-acceptance and confidence.
• The researchers found that gender norms, visible disability, community misconceptions, travel costs, and economic constraints limited wider social change.
• The study used thematic analysis supported by NVivo 14 and interpreted findings through the Social Ecological Model, which spans individual, interpersonal, community, institutional, and structural levels.

The PLOS study found that participatory storytelling was associated primarily with reduced internal and anticipated stigma rather than a complete removal of community stigma. Participants described greater confidence, self-respect, communication ability, and willingness to speak openly about their experiences.

The researchers combined 60 to 90 minute in-depth interviews with photovoice, a participatory method in which participants created and discussed photographs representing their experiences. Data collection took place at the Marie Adelaide Leprosy Centre in Karachi between June 2 and June 25, 2025, with live interpretation from Urdu into English.

The study reported that peer support groups created psychological safety and belonging, while organisational endorsement helped participants move from being treated as patients to being recognised as storytellers and peer educators. The authors also reported that episodic contact events did not fully overcome persistent misconceptions, gender restrictions, visible disability stigma, and financial barriers.

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 attributed this quotation to participant P06, a male government worker in his 40s.

Another participant explained the value of peer connection by saying, "if we are together and unite, so we can do a better work." The PLOS study attributed this quotation to participant P12, a self-employed man in his 60s.

Health researchers should treat the study as evidence of perceived empowerment and possible mechanisms of change, not as proof of causal effectiveness. The PLOS authors explicitly noted the absence of baseline and comparator data, the small purposive sample, possible social desirability bias, and translation-related complexity.

AI qualitative analysis can help researchers preserve the study's multi-level logic by coding interview and photovoice transcripts against both inductive themes and deductive framework categories. In this case, useful code families would include internal stigma, anticipated stigma, enacted stigma, intrapersonal empowerment, peer solidarity, institutional recognition, gender barriers, visible disability, economic constraints, and community contact.

Cross-segment analysis is particularly important for this type of research. The PLOS study reported that women faced permission and mobility constraints, while participants with visible Grade 2 Disability described persistent public scrutiny. Comparing themes by gender, disability status, age, employment, and participation method can show where an intervention works unevenly.

Researchers should use AI to accelerate discovery and comparison, while retaining human review for culturally specific meanings, translated language, sensitive disclosures, and contradictory cases. The PLOS research team itself used repeated listening, translator checks, codebook review, consensus discussions, and triangulation between interviews and photovoice.

The November 2, 2026 PLOS study shows how participatory storytelling can help people affected by leprosy move from shame and concealment toward confidence, connection, and narrative ownership, while also showing that individual empowerment does not automatically produce community-level change.

For researchers, the study demonstrates the value of combining interviews, photovoice, framework-based coding, inductive analysis, and cross-segment comparison. AI qualitative analysis can make that workflow faster and more traceable when human researchers continue to validate meanings, quotations, translations, and limitations.

Teams working with interview transcripts, open-ended surveys, and programme documents can use Evidano to organize evidence, identify themes, compare participant segments, and query analyses in one workspace. Try Evidano for free to explore an AI-assisted qualitative research workflow.