This post explains how a new qualitative study in PLOS ONE (Alam & Al-Mamun, published July 24, 2026) documents the everyday barriers to menstrual health and hygiene (MHH) in Khulna Railway Slum and what those findings mean for qualitative researchers and program teams. According to the PLOS ONE article published July 24, 2026, the authors conducted 18 in-depth interviews and 5 key informant interviews between 01/09/2025 and 31/10/2025, which generated themes linking inadequate WASH, socio-cultural stigma, and gendered resource gaps to menstrual dignity. Researchers and implementers who do qualitative analysis of menstrual health can use AI tools to accelerate transcription, coding, and cross-segment synthesis while preserving participant confidentiality.
Key Takeaways
The PLOS ONE study PLOS ONE (Alam & Al-Mamun, published July 24, 2026) found that structural-environmental constraints, socio-cultural stigma, and gendered inequities jointly shape menstrual experiences in Khulna Railway Slum.
- Data collection occurred between 01/09/2025 and 31/10/2025 and included 18 in-depth interviews (IDIs) and 5 key informant interviews (KIIs), according to the PLOS ONE article published July 24, 2026.
- The PLOS ONE study reported participants aged 15–45 years, with 39% in the 20–30 age band and 67% married, which highlights age and household-role patterns reported in the dataset.
- Participants in the PLOS ONE study described water cuts, shared toilets without doors, and hidden disposal practices; one participant said, “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 WASH failures translate into daily indignity.
What happened and how the PLOS ONE study was done
The PLOS ONE study directly asked how women and adolescent girls experience menstruation within Khulna’s informal settlements and analyzed the answers using thematic methods.
According to the PLOS ONE article published July 24, 2026, data were collected between 01/09/2025 and 31/10/2025 via 18 in-depth interviews with women aged 15–45 and 5 key informant interviews, and transcripts were translated and coded using NVivo v12 as described in the Methods section of the PLOS ONE article.
According to the PLOS ONE article published July 24, 2026, the authors used a Feminist Political Ecology framework and a combined inductive-deductive thematic analysis to identify three core domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities in access to menstrual resources.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 01/09/2025–31/10/2025 | Interviews conducted | 18 IDIs; 5 KIIs | Qualitative saturation reached, dataset supports thematic synthesis (PLOS ONE, July 24, 2026) |
| July 24, 2026 | Publication | PLOS ONE article (Alam & Al-Mamun) | Peer-reviewed, open access evidence for program and research planning |
| 2025 sample characteristics | Age range | 15–45 years (39% aged 20–30) | Highlights adolescent and reproductive-age perspectives (PLOS ONE, 2026) |
| 2025 participant data | Marital status | 67% married | Signals household power and resource dynamics affecting MHH decisions (PLOS ONE, 2026) |
| 2025 field notes | Average interview length | 25–30 minutes | Concise narratives suitable for focused thematic coding (PLOS ONE, 2026) |
Implications for qualitative researchers and program teams
Answer: Qualitative researchers should treat menstrual health as a socio-environmental phenomenon, not only a biological one, and design methods accordingly.
According to the PLOS ONE article published July 24, 2026, the study shows that MHH problems in Khulna combine WASH failures, stigma, and poverty, which means researchers should collect layered contextual data (infrastructure, social norms, and household economics) rather than single-dimension surveys.
According to the PLOS ONE article published July 24, 2026, adolescent girls reported missed school and social exclusion, so program teams should measure outcomes beyond product uptake (for example, school attendance, dignity, and safety).
According to the PLOS ONE article published July 24, 2026, disposal practices and privacy concerns emerged as program-relevant themes, indicating that interventions should include waste systems and lighting/locks for shared toilets.
How Evidano helps with qualitative analysis of menstrual health
Problem: audio transcripts and translation are slow and error prone → Solution: fast, accurate transcription
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
According to the PLOS ONE article published July 24, 2026, interviews were audio-recorded and transcribed and then translated into English, which is a time-consuming step that AI transcription with custom dictionaries can speed while preserving terminology from local languages.
Evidano’s transcription capabilities, including custom dictionaries and PII redaction, reduce manual hours in the transcription stage and keep sensitive participant content secure; see Evidano speech-to-text for details.
Problem: thematic coding across small samples is labor intensive → Solution: thematic + cross-segment analysis
Answer: Evidano automates thematic code suggestion and cross-segment frequency analysis to accelerate pattern discovery.
According to the PLOS ONE article published July 24, 2026, the authors used NVivo and a combined inductive-deductive approach; Evidano complements that workflow by producing preliminary codebooks, co-occurrence networks, and segment comparisons so researchers can focus on interpretation rather than repetitive coding.
Learn more about automated analysis in the Evidano features page.
Problem: protecting confidentiality while sharing findings → Solution: secure, shareable summaries
Answer: Evidano supports encrypted storage and de-identification so teams can share analytic outputs without exposing raw transcripts.
According to the PLOS ONE article published July 24, 2026, the authors removed participant names and used codes like IDI-08 to protect anonymity; Evidano provides similar de-identification and role-based access to safeguard sensitive MHH data.
FAQ: qualitative analysis menstrual health
How many interviews did the PLOS ONE study use and is that enough for qualitative inference?
Answer: The PLOS ONE study used 18 in-depth interviews and 5 key informant interviews between 01/09/2025 and 31/10/2025, and the authors report reaching thematic saturation.
According to the PLOS ONE article published July 24, 2026, saturation was assessed iteratively with three additional IDIs after the fifteenth interview and no new themes emerged, which supports analytical rather than statistical generalization.
What were the main barriers to menstrual dignity identified by the PLOS ONE study?
Answer: The PLOS ONE study identified three interrelated domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities in resource access.
According to the PLOS ONE article published July 24, 2026, examples include water scarcity, shared toilets without privacy, hidden disposal practices, intergenerational silence, and limited household budgets for pads.
How can AI improve coding reliability in MHH qualitative studies?
Answer: AI can standardize initial code suggestions, surface co-occurrence patterns, and quantify code frequencies across segments to increase transparency and reproducibility.
According to the PLOS ONE article published July 24, 2026, the authors used NVivo for coding and consensus meetings to resolve discrepancies; Evidano-style AI-assisted codebooks can speed the initial coding and free researchers to spend more time on reflexive interpretation.
Can AI tools preserve participant confidentiality in sensitive MHH transcripts?
Answer: Yes, when platforms include PII redaction, encrypted storage, and access controls.
According to the PLOS ONE article published July 24, 2026, the research team removed names and used codes like IDI-08 to protect anonymity; platforms with built-in de-identification replicate that practice and reduce manual risk.
Conclusion & Next Steps
According to the PLOS ONE article published July 24, 2026, menstrual dignity in Khulna’s informal settlements is constrained by WASH gaps, stigma, and poverty, and addressing those issues requires integrated infrastructure, education, and product access.
AI-enabled qualitative analysis can shorten the path from raw recordings to policy-ready themes by automating transcription, suggesting code frameworks, and producing cross-segment visualizations that preserve confidentiality.
If you run qualitative MHH research or program evaluations, consider piloting AI-assisted workflows to accelerate insight while maintaining ethical safeguards; for a hands-on start, Try Evidano for free.