AI-enabled Qualitative Analysis of Menstrual Health

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.

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).

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.

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.