This post explains how AI-enabled qualitative research methods accelerate synthesis of studies on menstrual health, aimed at qualitative researchers, NGOs, and WASH program teams. The primary keyword is "qualitative analysis of menstrual health" and this article shows concrete steps to extract themes, quotes, and policy-relevant metrics from interviews. An ethical note: this guidance is research-focused and non-diagnostic; any clinical inference should follow local ethics and health protocols.
Key Takeaways
According to the PLOS One study published July 24, 2026, the Khulna Railway Slum qualitative study found that menstruation experiences are shaped by three interrelated domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities in access to resources (PLOS One).
- The PLOS One study collected 18 in-depth interviews (IDIs) and 5 key informant interviews (KIIs) between 01/09/2025 and 31/10/2025, achieving thematic saturation by the 15th IDI.
- The PLOS One sample included participants aged 15–45 with 39% in the 20–30 age bracket and 67% married, and 72% reported >8 years residence in the slum (published July 24, 2026).
- The PLOS One team reported pervasive WASH failures: intermittent water, shared toilets without privacy, and absence of safe menstrual waste disposal; participants described hiding used cloths and ‘‘waiting hours’’ for water (quotes from IDI participants in the study).
What happened: PLOS One Khulna study methods and findings
Answer: According to the PLOS One article published July 24, 2026, researchers conducted 18 IDIs and 5 KIIs in Khulna Railway Slum between September and October 2025 using a Feminist Political Ecology framework to code thematic domains.
According to the PLOS One article published July 24, 2026, the authors combined inductive and deductive thematic analysis, used NVivo v12, and applied COREQ guidance with ethical approval on 10 August 2025 from the IRB at Gopalganj Science and Technology University.
According to the PLOS One article published July 24, 2026, three major themes emerged: structural-environmental constraints (water scarcity, poor sanitation, waste disposal gaps, overcrowded housing), socio-cultural stigma (shame, silence, mobility restrictions), and gendered inequities in resource access (period poverty, male-controlled household budgets).
According to the PLOS One article published July 24, 2026, participant testimony included direct phrases such as "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), illustrating the lived hygiene burden.
According to the PLOS One article published July 24, 2026, other verbatim reflections were recorded, for example "The toilet near our room has no door. Boys and men walk by all the time. I feel scared to change my clothes there" (IDI-04, student), which exposes safety and privacy gaps.
Findings snapshot
| Date / Timing | Metric | Value | Implication |
|---|---|---|---|
| 01/09/2025–31/10/2025 | Interviews collected | 18 IDIs; 5 KIIs | Qualitative depth with iterative coding and saturation |
| 10 August 2025 | Ethics approval | IRB Approval No. 473478-FY 2024–2025 | Ethics procedures for consent, minors, and confidentiality |
| Published 24 July 2026 | Participant demographics | Ages 15–45; 39% age 20–30; 67% married; 72% >8 years residence | Enables age- and tenure-segmented thematic interpretation |
| July 24, 2026 | Primary thematic domains | 3 domains: environmental constraints; stigma; gendered inequities | Use targeted WASH + education + affordable product interventions |
Implications for qualitative researchers and program teams
Answer: Researchers should prioritize context-rich coding, participant voice extraction, and cross-segment comparisons when studying menstrual health in informal settlements.
According to the PLOS One article published July 24, 2026, qualitative findings linked infrastructural failures to psychosocial outcomes; researchers therefore must code across domains (WASH, stigma, economics) rather than treating menstruation as an isolated health variable.
According to the PLOS One article published July 24, 2026, adolescent girls reported school absenteeism tied to sanitation and stigma; program evaluators should therefore measure both behavioral outcomes (attendance) and experiential outcomes (shame, fear) using mixed qualitative indicators.
Practically, teams should collect short demographic metadata (age band, years in community, marital status) to enable cross-segment analysis, because the PLOS One sample showed meaningful variation by age and tenure (published July 24, 2026).
How Evidano helps with AI-enabled qualitative analysis
Problem: Slow synthesis of interview data → Solution: Thematic automation
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Problem: Manual coding of 18 IDIs and 5 KIIs, as in the PLOS One study, can take weeks to reach consensus; Solution: Evidano speeds thematic coding by suggesting codes, extracting verbatim quotes, and mapping themes to pre-defined conceptual domains such as the study's structural, stigma, and gendered-resource domains. See Evidano features for automated thematic workflows.
Problem: Non-English transcripts and translation risk → Solution: Transcription + translation
Problem: The PLOS One team recorded interviews in Bengali and translated transcripts for analysis; Solution: Evidano provides transcription and translation with custom dictionaries and PII redaction to preserve participant confidentiality and semantic fidelity, reducing human re-translation work. See Evidano speech-to-text for details.
Problem: Need for cross-segment frequency and quote retrieval → Solution: Searchable codebooks and AI chat
Problem: Extracting how many participants mentioned "water scarcity" or retrieving representative quotes is time consuming; Solution: Evidano generates code frequency tables, co-occurrence networks, and allows AI chat over your dataset so teams can ask: "Show me all quotes about disposal practices among women over 30."
Problem: Ethical data handling in sensitive research → Solution: Encrypted, private analysis
Problem: The PLOS One authors restricted full transcripts to protect participants; Solution: Evidano supports encrypted project storage and PII redaction so minimal datasets and de-identified excerpts can be prepared for sharing under IRB constraints. See Evidano data security.
FAQ: qualitative analysis of menstrual health
How can AI help extract themes from small qualitative samples like the Khulna study?
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Answer: AI can accelerate coding, suggest emergent themes, and surface representative quotations, while leaving final interpretation to researchers.
AI tools can rapidly generate code suggestions from 18 IDIs and 5 KIIs, highlight code frequencies, and pull context for each quote so researchers validate themes rather than replacing human judgment.
What concrete metrics should researchers report for menstrual health qualitative studies?
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Answer: Report sample size, data collection dates, participant demographics (age bands, marital status, years in community), ethics approval, and saturation point.
For example, the PLOS One study reported 18 IDIs, 5 KIIs, collection between 01/09/2025–31/10/2025, IRB approval on 10 August 2025, and that saturation occurred by the 15th IDI (PLOS One, published July 24, 2026).
Can AI preserve participant confidentiality in sensitive menstrual health data?
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Answer: Yes, when used with PII redaction and encrypted storage, AI tools can help prepare de-identified excerpts for analysis and sharing.
Evidano supports PII redaction at transcription time and encrypted project-level controls to align with IRB restrictions like those described in the PLOS One data availability statement.
Should qualitative menstrual health studies include WASH and stigma questions?
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Answer: Yes, include WASH access, disposal practices, privacy, and experiences of stigma to capture the multi-dimensional nature of menstruation.
The PLOS One Khulna study demonstrated that infrastructural constraints, cultural taboos, and economic barriers interact to shape menstruation experiences, so mixed thematic prompts yield richer policy insights.
Conclusion & Next Steps
The PLOS One Khulna study (published July 24, 2026) shows that menstrual health in informal settlements is a combined problem of infrastructure, stigma, and gendered resource access, and AI-assisted qualitative analysis shortens the path from interviews to actionable insights.
Teams evaluating MHH programs should collect short demographic metadata, audio-record interviews with consent, and prepare de-identified minimal datasets to enable rapid thematic synthesis.
To accelerate your next qualitative synthesis, Try Evidano for free and evaluate automated transcription, thematic coding, and secure collaboration for sensitive health research.
Topics
- qualitative analysis of menstrual health
- AI qualitative analysis
- menstrual health qualitative study
- menstrual hygiene research AI
Keep reading
- Commentary on NewsAI synthesis: Qualitative analysis of menstrual healthUse AI-enabled qualitative analysis to extract actionable insights from a PLOS ONE study on menstrual health in Khulna slums. Read findings, quotes, and research-ready next steps.
- Commentary on NewsActionable Qualitative Analysis: Menstrual Health in KhulnaHow to conduct qualitative analysis of menstrual health interviews from Khulna slums using AI-enabled tools; learn methods, stats, and how Evidano speeds synthesis.
- Commentary on NewsMenstrual Health Qualitative Analysis: Khulna StudyPractical synthesis of the PLoS One Khulna study (Jul 24, 2026) for qualitative analysis of menstrual health. Learn methods, numbers, and how Evidano speeds thematic research.