AI qualitative analysis menstrual health is essential for turning small, rich interview datasets into clear, actionable findings for program design and policy. According to the PLOS One study published July 24, 2026, women and adolescent girls in the Khulna Railway Slum face intersecting barriers from water scarcity, inadequate sanitation, and socio-cultural stigma. This post is written for qualitative researchers, NGOs, and WASH program leads who want step-by-step guidance on using AI-enabled tools to reproduce the study's rigor, extract quotations, measure code frequency, and report segment-level differences.
Key Takeaways
According to the PLOS One study published July 24, 2026, 18 in-depth interviews and 5 key informant interviews collected between 01/09/2025 and 31/10/2025 reveal that menstrual experiences in Khulna slums are driven by structural-environmental constraints, socio-cultural stigma, and gendered inequities in resource access. PLOS One
- 18 in-depth interviews and 5 key informant interviews were conducted between 01 September 2025 and 31 October 2025, per the PLOS One study published July 24, 2026.
- Participant demographics in the PLOS One study included 39% aged 20–30 years and 67% married, and the authors report 39% had minimal or no schooling, 33% had secondary education, and 28% had primary education.
- According to the PLOS One findings published July 24, 2026, 72% of participants had lived in the slum for over eight years, reinforcing long-term exposure to WASH deficits and overcrowding.
- Multiple participants told the research team in quotes 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 how infrastructure and stigma combine to reduce menstrual dignity.
What Happened / How It Works
What happened: the PLOS One qualitative study (Alam & Al-Mamun) conducted 18 in-depth interviews and 5 key informant interviews in the Khulna Railway Slum between 01 September 2025 and 31 October 2025 to explore lived menstrual experiences. According to the PLOS One article published July 24, 2026, the authors used semi-structured interviews in Bengali, transcribed and translated the data, and applied a combined inductive-deductive thematic analysis guided by Feminist Political Ecology.
According to the PLOS One study published July 24, 2026, the authors reached thematic saturation by the 15th interview and analyzed data with NVivo v.12, triangulating IDIs and KIIs and following COREQ reporting standards.
According to the PLOS One findings published July 24, 2026, three mutually reinforcing conceptual domains structured the results: (1) structural-environmental constraints (water, sanitation, waste disposal, overcrowding), (2) socio-cultural stigma (shame, silence, restricted mobility), and (3) gendered inequities in access to menstrual resources (cost, household decision-making).
Findings Snapshot
| Date | Metric | Value (from PLOS One) | Implication |
|---|---|---|---|
| 01/09/2025–31/10/2025 | Data collected | 18 IDIs, 5 KIIs | Small, in-depth sample suitable for thematic, contextual analysis |
| Published 24/07/2026 | Lead result | Three domains: structural, socio-cultural, gendered inequity | Design interventions that combine WASH upgrades, education, and product access |
| Participant demographics (as reported) | Education distribution | 39% minimal/no schooling, 33% secondary, 28% primary | Low formal education rates imply need for oral, visual educational materials |
| Participant residency | Time in slum | 72% > 8 years | Long-term exposure suggests structural fixes, not short-term relief |
Implications for qualitative researchers and program teams
Implication: qualitative teams should combine thematic depth with frequency and segment analyses to inform multi-component interventions. According to the PLOS One study published July 24, 2026, the layered problems in Khulna require WASH upgrades, stigma reduction, and subsidized product access rather than single-focus programs.
- Design: Use sample stratification to compare adolescents (15–19) versus adult women (20–45), as the PLOS One study did, because 39% of participants were in their 20s and adolescents reported distinct school absenteeism drivers.
- Monitoring: Track code frequency and co-occurrence (for example, 'water scarcity' + 'shame') monthly to measure whether WASH investments reduce stigma-linked absenteeism, following the PLOS One emphasis on structural-social interaction.
- Reporting: Include verbatim quotes to preserve voice; the PLOS One paper uses quotations such as “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) to illustrate lived risk.
How Evidano Helps
Problem: manual synthesis of small qualitative datasets is slow
How Evidano helps: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano speeds coding and synthesis by auto-extracting themes, surfacing high-value quotations, and showing co-occurrence networks so teams can move from 18 interviews to program-ready insights in days instead of weeks.
Evidano supports secure transcription, PII redaction, and bilingual translation workflows, useful because the PLOS One team recorded interviews in Bengali and translated transcripts for analysis.
Problem: tracking theme frequency and subgroups is error-prone
How Evidano helps: Evidano automates frequency counts and cross-segment comparisons (for example, adolescents vs adults), replicating the stratified interpretation used in the PLOS One study.
Use Evidano’s thematic and cross-segment analysis to quantify how often 'water scarcity' co-occurs with 'school absenteeism' and export reproducible charts for funders and municipal partners. For product details see the Evidano features page.
Problem: protecting participant confidentiality in sensitive research
How Evidano helps: Evidano encrypts data and offers PII redaction during transcription and analysis, matching the ethical protections described by the PLOS One authors and facilitating IRB-compliant workflows.
Teams can request controlled exports of de-identified quotes or minimal datasets comparable to the PLOS One minimal dataset approach while maintaining participant anonymity.
FAQ: AI qualitative analysis menstrual health
What is AI qualitative analysis for menstrual health research?
AI qualitative analysis for menstrual health research is the use of machine learning and language models to assist coding, theming, and extracting quotations from interview and focus group transcripts.
According to the PLOS One study published July 24, 2026, rigorous qualitative work combines human-led thematic interpretation with tools (the PLOS One authors used NVivo) and benefits from automation for speed and reproducibility.
Can AI tools analyze small samples like the 18 interviews in the PLOS One study?
Yes, AI tools can accelerate analysis of small, rich datasets while preserving depth. AI tools help surface patterns and quotations but should not replace researcher-led interpretation.
The PLOS One study achieved thematic saturation by interview 15, demonstrating that small samples can be analytically sufficient when combined with iterative coding; AI can make that iteration faster and more transparent.
How do I preserve participant voice and ethical standards when using AI?
Preserve voice and ethics by combining human verification with automated extraction and by applying PII redaction before analysis. Always document translation checks and consent processes.
The PLOS One research recorded interviews in Bengali, translated with back-checking, and followed IRB approval (Approval No. 473478-FY 2024–2025; Approval Date: 10 August 2025), showing the ethical steps to mirror when using AI-assisted workflows.
How can I extract quotable evidence and code frequencies for funders?
Extract quotable evidence by tagging high-confidence verbatim quotes and linking them to codes, then export a minimal dataset with context and de-identification for reviewers.
The PLOS One paper includes multiple short quotations attributed to participant codes (for example, IDI-08) as examples of how to present voice-linked evidence alongside aggregate code counts.
Conclusion & Next Steps
According to the PLOS One study published July 24, 2026, menstrual health in Khulna’s informal settlements is shaped by water scarcity, inadequate sanitation, disposal gaps, stigma, and constrained household budgets; addressing these issues requires integrated WASH, education, and product-access interventions.
Qualitative researchers and program teams can replicate the study’s rigor faster by using AI-enabled platforms to transcribe in local languages, maintain translation checks, perform thematic plus cross-segment analyses, and export IRB-friendly minimal datasets.
If you want to convert interview transcripts into program-ready evidence quickly, Try Evidano for free. For platform details see Evidano features.