This post explains what a PLOS One study of menstrual health in Khulna’s informal settlements means for researchers doing qualitative analysis of menstrual health, and how AI-enabled workflows speed trustworthy synthesis. According to the PLOS One study published July 24, 2026, the authors used 18 in-depth interviews and five key informant interviews collected between 01/09/2025 and 31/10/2025 to map three core domains that shape menstrual experiences. Researchers and program teams reading qualitative findings need practical, reproducible ways to extract themes, quantify occurrence, and compare subgroups; the primary keyword for this post is "qualitative analysis of menstrual health." Ethics note: this summary is research-focused and non-diagnostic.
Key Takeaways
According to the PLOS One article published July 24, 2026, 18 in-depth interviews (IDIs) and five key informant interviews (KIIs) in Khulna Railway Slum found that menstruation is shaped by three interrelated domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities in access to resources. PLOS One.
- 1) The PLOS One study (published July 24, 2026) collected data between 01/09/2025 and 31/10/2025 from 18 participants aged 15–45 and 5 local stakeholders.
- 2) The PLOS One study reports participant education levels as about 39% non-literate/minimal, 33% secondary, and 28% primary (sample reported in July 2026).
- 3) The PLOS One study (Khulna context) documents WASH problems, private-space scarcity, and undisposed menstrual waste as drivers of unsafe practices in the settlements studied in 2025.
- 4) For qualitative teams, reproducing this depth of insight requires coded transcripts, transparent codebooks, and frequency counts; AI tools can accelerate thematic coding and cross-segment counting without exposing data to third-party model training.
What the PLOS One study found
The PLOS One article (Alam & Al-Mamun, published July 24, 2026) found that menstrual experiences in Khulna Railway Slum are driven by structural-environmental constraints, socio-cultural stigma, and gendered inequities in access to products and sanitation.
The PLOS One study collected qualitative data between 01/09/2025 and 31/10/2025 via 18 IDIs with women and adolescent girls aged 15–45 and five KIIs with community stakeholders, and the authors analyzed transcripts using NVivo v.12 with a combined inductive-deductive thematic approach.
The PLOS One study documents concrete obstacles: participants reported water scarcity, communal toilets without locks or lighting, no designated menstrual-waste disposal, overcrowded single-room housing, and stigma that limits mobility and product purchase.
"When water stops coming from the tap, I wait for hours. If it doesn’t come, I can’t clean myself properly, " an IDI participant said in the PLOS One study (IDI-08, Housewife).
The PLOS One article also records that 72% of participants had lived in the slum more than eight years and that household financial constraints often prioritized other expenses over menstrual products (findings reported July 2026).
Snapshot table: key numbers to extract and report
| Date / Source | Metric | Value | Implication for qualitative analysis |
|---|---|---|---|
| 01/09/2025–31/10/2025 (PLOS One) | Interviews conducted | 18 IDIs + 5 KIIs | Use transcript-level IDs to cross-tab themes by age, occupation, and tenure |
| July 24, 2026 (PLOS One) | Participant age range | 15–45 years | Stratify codes for adolescent vs adult experiences |
| July 24, 2026 (PLOS One) | Education distribution | ≈39% minimal/non-literate; 33% secondary; 28% primary | Correlate knowledge/misperception codes with education strata |
| 2025 data (PLOS One) | Residency in slum | 72% >8 years | Consider place-based codes (long-term vs recent residents) |
| July 24, 2026 (PLOS One) | Core thematic domains | 3 domains: structural-environmental, socio-cultural stigma, gendered inequities | Map a codebook hierarchy (domain → theme → subcode) for reproducible counts |
Implications for qualitative researchers using this study
For qualitative researchers, the PLOS One study shows that small, purposive samples can produce rich, policy-relevant themes when paired with transparent coding and reflexivity.
The PLOS One authors report using COREQ guidance and NVivo v.12 for coding, and they carried out independent double-coding followed by consensus meetings, methods you should cite and emulate when conducting similar work.
The PLOS One study achieved thematic saturation by interview 15 and continued to 18 to confirm saturation; therefore, teams replicating similar urban-informal-settlement work should plan for at least 15–25 IDIs while allowing KIIs for triangulation.
The PLOS One findings imply that analysts should convert qualitative themes into extractable metrics: count occurrences of codes, cross-tab themes with participant demographics, and report absolute sample counts alongside quotes (the PLOS One article provides numerous verbatim quotes tied to participant IDs).
The PLOS One study includes an IRB approval record (Approval No. 473478-FY 2024–2025; Approval Date: 10 August 2025), which underscores the importance of documented ethical approvals and data protection in menstruation research.
How Evidano helps (problem → solution)
What is Evidano?
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Problem: Manual coding is slow and hard to reproduce → Solution: Evidano accelerates thematic coding and produces an auditable codebook so teams can reproduce the PLOS One style of analysis faster and with traceable decisions.
Problem: Counting and cross-segment comparison are laborious → Solution: Evidano generates frequency tables and cross-segment analyses from coded transcripts so analysts can report absolute counts (for example, how many participants reported water scarcity) alongside verbatim quotes.
Problem: Translation, transcription, and PII handling are sensitive in field studies → Solution: Evidano offers transcription and translation with custom dictionaries and PII redaction, reducing manual errors while preserving confidentiality.
For more on capabilities, see the Evidano features page.
How to mirror PLOS One rigor with Evidano
Map PLOS One methods to workflow: import audio and transcripts, apply an initial deductive codebook for the three domains, run AI-assisted coding to surface emergent subcodes, then perform human review and consensus, all steps Evidano supports.
Evidano keeps data encrypted and does not use customer data to train third-party models, which aligns with ethics and IRB expectations when handling sensitive menstrual-health interviews (document policies at Evidano data security).
FAQ: qualitative analysis of menstrual health
How many interviews did the PLOS One study use and when were they collected?
Answer: The PLOS One study used 18 in-depth interviews and 5 key informant interviews collected between 01/09/2025 and 31/10/2025.
Supporting detail: The PLOS One article (published July 24, 2026) states the sample and dates explicitly and describes purposive plus limited convenience sampling to obtain information-rich participants.
What were the main themes identified in the PLOS One study?
Answer: The PLOS One study identified three interrelated domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities in access to resources.
Supporting detail: The PLOS One authors used a Feminist Political Ecology framework to structure deductive codes and added emergent codes through inductive analysis.
Can AI tools like Evidano reproduce the PLOS One analytic approach?
Answer: Yes, AI-assisted tools can reproduce key steps: transcript ingestion, deductive code application, emergent theme detection, and frequency cross-tabs while preserving human oversight.
Supporting detail: The PLOS One study used NVivo for coding and human consensus; AI tools speed coding and counting but should be paired with human validation and documented reflexivity, as the PLOS One authors recommend.
Are the PLOS One findings generalizable beyond Khulna’s Railway Slum?
Answer: No, the PLOS One authors state their qualitative findings are context-specific and intended for analytical transferability rather than statistical generalization.
Supporting detail: The PLOS One article notes the sample size and purposive sampling approach and recommends mixed-method or larger-scale studies to test prevalence across other settings.
Conclusion & Next Steps
The PLOS One study (published July 24, 2026) offers a clear, replicable qualitative account showing how WASH constraints, stigma, and poverty shape menstrual health in Khulna’s informal settlements.
For researchers, the practical next step is to combine transparent codebooks, absolute counts, and verbatim quotes when reporting findings; for program teams, the next step is to map themes to targeted WASH and education interventions.
If your team needs to speed coding, produce cross-segment frequency analyses, and maintain an auditable trail for ethics boards, Evidano can support those steps; learn more on our features page.
Ready to test an AI-assisted qualitative workflow? Try Evidano for free.