The primary keyword for this post is qualitative analysis of menstrual health, aimed at qualitative researchers, program teams, and NGOs who analyze interviews and reports. According to the July 24, 2026 PLOS One study, researchers used 18 in-depth interviews and five key informant interviews collected between 01/09/2025 and 31/10/2025 to explore menstrual experiences in Khulna Railway Slum, Bangladesh (PLOS One). The payoff: this post shows how AI-enabled qualitative research methods can make thematic synthesis faster, reproducible, and ethically safer while preserving the close reading that grounded the PLOS One findings.
Key Takeaways
According to the July 24, 2026 PLOS One study, women and adolescent girls in Khulna Railway Slum experienced menstruation as a socially and structurally shaped hardship driven by inadequate WASH, stigma, and economic constraints (PLOS One). The PLOS One study collected 18 in-depth interviews and 5 key informant interviews between 01/09/2025 and 31/10/2025 and used thematic analysis to identify three core domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities.
- 18 in-depth interviews and 5 KIIs were conducted between 01/09/2025 and 31/10/2025, according to the PLOS One article published on July 24, 2026.
- Khulna district has about 2.61 million residents and roughly 20% live in slums, as reported in the PLOS One study.
- By the fifteenth interview the authors reported thematic saturation and continued to 18 IDIs to confirm patterns, according to the Methods section of the PLOS One paper.
- Women described practical harms: water scarcity, shared toilets without doors, and hidden disposal practices that increase health risk, as quoted in the PLOS One findings.
What happened: study design and core findings
The direct answer: the PLOS One study used an interpretivist qualitative design to document lived menstrual experiences in Khulna Railway Slum between September and October 2025, using thematic analysis guided by Feminist Political Ecology (PLOS One).
The PLOS One study recruited 18 women and adolescent girls aged 15–45 and stratified participants into adolescent (15–19) and adult (20–45) groups to capture age-sensitive differences, as described in the Methods section of the PLOS One article.
The PLOS One authors identified three interrelated domains shaping menstrual experiences: (1) structural-environmental constraints such as limited water and private toilets, (2) socio-cultural stigma that enforces concealment, and (3) gendered inequities limiting access to paid menstrual products, per the Findings and Discussion in PLOS One.
Representative verbatim evidence from the PLOS One interviews includes: "When water stops coming from the tap, I wait for hours. If it doesn’t come, I can’t clean myself properly, " attributed to IDI-08 (Housewife) in the PLOS One paper.
The PLOS One authors also recorded disposal-related secrecy: "I wrap the used cloth in paper and throw it away at night. I don’t want anyone to see it; people will gossip, " attributed to IDI-10 (Tailor) in the PLOS One findings.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 01/09/2025–31/10/2025 | In-depth interviews | 18 participants (age 15–45) | Sufficient qualitative depth and reported saturation at interview 15, per PLOS One |
| 24 July 2026 | Publication date | PLOS One article published | Findings available for program design and replication |
| 2025 (cited data) | Khulna demographics | ≈2.61 million population; ~20% in slums | Contextualizes scale of WASH and housing constraints in study area |
| Participant demographics | Marital status and tenure | 67% married; 72% lived in slum >8 years | Indicates persistent, household-level gendered resource constraints |
Implications for qualitative researchers and program teams
The direct answer: qualitative researchers should combine rigorous close-reading with reproducible coding workflows so findings like those in the PLOS One study can inform targeted WASH and education programs.
The PLOS One study links environment, stigma, and poverty to menstrual outcomes, so program teams must plan integrated interventions that improve private sanitation, disposal systems, and community education rather than single-component distributions, as recommended in the Discussion and Policy Implications of the PLOS One paper.
Researchers replicating this study design should note the PLOS One procedural details: ethical approval (IRB Approval No. 473478-FY 2024–2025; Approval Date 10 August 2025), audio-recorded interviews conducted in Bengali with English translation and back-checking, and coding using NVivo v12 with independent coders and consensus meetings.
How Evidano helps: map problems to AI-enabled solutions
Problem: slow thematic synthesis across interviews
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Solution: Evidano's automated thematic coding accelerates the deductive and inductive steps the PLOS One team used, by ingesting transcripts and producing code frequencies, co-occurrence networks, and hierarchical code trees to surface the three domains reported in PLOS One.
Practical link: see the Evidano features page for thematic analysis and visualization capabilities.
Problem: transcription, translation, and confidentiality burdens
Solution: Evidano supports transcription with custom dictionaries and PII redaction and bidirectional translation to preserve semantic nuance from Bengali to English during coding, matching the PLOS One workflow of interview transcription and back-checking.
Practical link: learn about Evidano's speech tools on the Speech-to-Text page.
Problem: replicability and rapid policy reporting
Solution: Evidano exports reproducible codebooks, minimal de-identified data tables, and extractable quotable evidence so teams can produce policy briefs like the PLOS One recommendations while preserving participant confidentiality.
Security note: Evidano encrypts data and does not share it with third-party LLM training, supporting ethical reuse consistent with the PLOS One data availability constraints.
FAQ: qualitative analysis of menstrual health
How did the PLOS One team achieve thematic saturation with 18 interviews?
Answer: they used concurrent data collection and analysis and reported that no new themes emerged after the fifteenth interview, so they completed to 18 to confirm saturation, as described in the PLOS One Methods section.
Supporting detail: the PLOS One authors collected interviews from 01/09/2025 to 31/10/2025 and conducted ongoing coding and comparison to assess saturation.
Can AI reproduce the PLOS One codebook without losing nuance?
Answer: AI can reproduce and accelerate codebook generation, but human validation is required to preserve contextual nuance, as recommended by qualitative best practices and reflected in the PLOS One reflexivity approach.
Supporting detail: the PLOS One authors used independent coders and consensus meetings, a process AI-enabled platforms should support through reviewable suggestions and editable code hierarchies.
How should teams protect participant confidentiality when sharing qualitative outputs?
Answer: share de-identified excerpts, codebooks, and analytic memos rather than full transcripts, following the PLOS One data access approach and IRB restrictions described in the paper.
Supporting detail: the PLOS One authors provided a minimal de-identified dataset and directed requests to the Institutional Data Access Committee to protect participants.
Which outputs from AI-enabled qualitative analysis are most useful to NGOs and city planners?
Answer: thematic prevalence tables, co-occurrence visualizations, and quotable extracts mapped to recommendations are most actionable for policy teams, reflecting the kinds of evidence the PLOS One authors used to recommend WASH upgrades and education.
Supporting detail: the PLOS One Policy Implications recommend gender-responsive WASH, menstrual education, affordable products, and integration into urban programs.
Conclusion & Next Steps
The PLOS One study (published July 24, 2026) documents how inadequate WASH, stigma, and poverty shape menstruation in Khulna slums and provides a clear, context-specific evidence base for integrated interventions.
AI-enabled qualitative analysis can reproduce the PLOS One thematic rigor while speeding coding, producing extractable quotes, and generating visual evidence for program design.
If your team analyzes interview transcripts, consider tools that support transcription, translation, thematic and cross-segment analysis to turn lived experience into policy actions; for global guidance see the UNICEF guidance on menstrual health and hygiene.
Ready to test an AI platform on interview transcripts and open-ended survey data? Try Evidano for free.