This post explains how AI-enabled qualitative analysis of menstrual health can speed synthesis and make findings reproducible for researchers, NGOs, and WASH implementers. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One study published 24 July 2026, women and adolescent girls in Khulna Railway Slum face intertwined structural, cultural, and resource constraints when managing menstruation, and the study used 18 in-depth interviews and 5 key informant interviews collected between 01 September 2025 and 31 October 2025 to reach its conclusions (PLOS One). The remainder of this post translates that research into actionable steps for AI-enabled qualitative research teams and shows how specific Evidano features map to the study’s methods and outputs.
Key Takeaways
According to the PLOS One study published on 24 July 2026, qualitative fieldwork in Khulna Railway Slum used 18 in-depth interviews and 5 key informant interviews collected between 01 September 2025 and 31 October 2025 to identify three core domains shaping menstrual experience: structural-environmental constraints, socio-cultural stigma, and gendered inequities in resource access (PLOS One).
- 18 IDIs and 5 KIIs were conducted from 01 September 2025 to 31 October 2025, enabling thematic saturation by interview 15, according to the authors.
- The study reports participant age range 15–45 years and states that 72% of participants had lived in the slum for over eight years (PLOS One, 24 July 2026).
- Researchers found practical barriers like unreliable water, communal toilets without doors, and no sanitary waste disposal, which led to unsafe practices and social isolation (PLOS One, 24 July 2026).
- Direct testimony in the study captures daily trade-offs: “When water stops coming from the tap, I wait for hours. If it doesn’t come, I can’t clean myself properly” (IDI-08, Housewife, quoted in PLOS One).
What happened and how the study was measured
The study documented lived menstrual experiences in Khulna Railway Slum using qualitative methods, and the data were collected between 01 September 2025 and 31 October 2025, according to PLOS One (published 24 July 2026).
The authors conducted 18 in-depth interviews (IDIs) with women and adolescent girls aged 15–45 years and 5 key informant interviews (KIIs) with health workers, teachers, and NGO staff, and they report achieving thematic saturation by the fifteenth IDI (PLOS One, 24 July 2026).
Interviews were audio-recorded in Bengali, transcribed, translated to English, double-checked, and analyzed with a combined deductive and inductive thematic approach using Braun and Clarke’s six-phase framework and NVivo v.12, as described in PLOS One (24 July 2026).
Findings snapshot
| Date / Source | Metric | Value / Quote | Implication |
|---|---|---|---|
| 01 Sep–31 Oct 2025 / PLOS One | Interviews | 18 IDIs, 5 KIIs | Depth for thematic saturation; replicable sample frame |
| 24 Jul 2026 / PLOS One | Age range | 15–45 years (participants) | Includes adolescents and adult women for cross-age coding |
| 24 Jul 2026 / PLOS One | Residency | 72% lived in slum >8 years | Long-term exposure to infrastructural constraints |
| 24 Jul 2026 / PLOS One | Direct quote (example) | “When water stops coming from the tap, I wait for hours.” (IDI-08) | Links water insecurity to menstrual dignity and hygiene practices |
Implications for qualitative researchers studying menstrual health
If your research question is how infrastructure and stigma shape menstruation, then prioritize contextual sampling and combined IDI/KII designs as PLOS One did in its July 24, 2026 publication.
Researchers should record interview language and transcription choices explicitly: PLOS One conducted interviews in Bengali, transcribed and translated to English with back-checks, which supports transparency and reproducibility (PLOS One, 24 July 2026).
Coding strategies should mix deductive domains and inductive emergence: PLOS One used three predefined domains from Feminist Political Ecology and added emergent subcodes during NVivo coding, a pattern AI tools can accelerate by suggesting candidate themes from raw transcripts.
How Evidano helps: mapping study problems to AI-enabled solutions
Problem: time-consuming transcription and translation
Solution: Evidano automates transcription and supports custom dictionaries and PII redaction, reducing manual effort required to convert audio interviews into analyzable text.
Evidence: PLOS One recorded interviews in Bengali and used a two-step translation verification process; AI transcription plus a custom translation dictionary preserves speaker intent and reduces back-translation overhead. See Evidano speech-to-text features at Evidano Speech-to-Text.
Problem: coding consistency across coders
Solution: Evidano generates initial thematic code suggestions, supports hierarchical codebooks, and lets multiple analysts reconcile codes with audit trails to mirror the NVivo-led consensus approach described in PLOS One (24 July 2026).
Practical benefit: The PLOS One team resolved discrepancies through consensus meetings; Evidano preserves those reconciliation steps and timestamps to strengthen dependability and confirmability.
Problem: extracting cross-segment patterns (adolescents vs adults)
Solution: Evidano performs cross-segment frequency and co-occurrence analysis so teams can quantify how themes like ‘water scarcity’ and ‘school absenteeism’ overlap for ages 15–19 versus 20–45.
Product link: learn more about Evidano thematic and cross-segment analyses at Evidano Features.
Problem: ethical handling of sensitive transcripts
Solution: Evidano provides PII redaction and encrypted storage and does not use customer data to train third-party models, matching the confidentiality concerns and IRB constraints the PLOS One study described when limiting public transcript access (PLOS One, 24 July 2026).
If you need structured sharing to an Institutional Review Board, Evidano’s exportable minimal datasets support safe requests similar to those described by the PLOS One authors.
FAQ: qualitative analysis of menstrual health
How many interviews are enough for thematic saturation in menstrual health research?
Answer: The PLOS One study reached thematic saturation by the 15th in-depth interview and completed 18 IDIs total, which suggests 12–20 rich IDIs plus a few KIIs is often sufficient in similar contexts.
Support: According to the PLOS One article published 24 July 2026, the authors judged saturation was achieved at interview 15 and added three more interviews to confirm no new themes emerged.
What transcription and translation steps matter for reproducibility?
Answer: Record interviews in the participant language, transcribe verbatim, translate with a verification pass, and document each step, as PLOS One did for Bengali-to-English translation.
Support: The PLOS One authors recorded in Bengali, transcribed and translated, and then applied a two-step verification to preserve semantic accuracy (PLOS One, 24 July 2026).
Can AI tools bias thematic results in stigmatized topics like menstruation?
Answer: AI tools can introduce bias if training data do not reflect the study population, so researchers should use adjustable dictionaries and human-in-the-loop validation.
Support: The PLOS One study emphasized reflexivity and team consensus in coding; similarly, Evidano recommends analyst review of AI-suggested codes to avoid mislabeling culturally specific phrases.
How should researchers handle sensitive qualitative data ethically?
Answer: Use IRB-approved consent, pseudonymize transcripts, restrict raw transcript sharing, and provide a controlled minimal dataset for verification, matching the approach in PLOS One.
Support: PLOS One limited public access to full transcripts for confidentiality, offering a minimal de-identified dataset on request and describing IRB approval dated 10 August 2025 (PLOS One, published 24 July 2026).
Conclusion & Next Steps
The PLOS One study published 24 July 2026 shows that qualitative data from 18 IDIs and 5 KIIs can reveal how water, sanitation, stigma, and poverty interact to shape menstrual health in urban informal settlements (PLOS One).
AI-enabled qualitative research platforms can accelerate transcription, suggest thematic codes, and produce cross-segment analyses while preserving ethical safeguards described in the study.
If you run qualitative studies on menstrual health or other sensitive topics, try turning raw audio and transcripts into reproducible, auditable findings with Evidano.
Get started: Try Evidano for free.