AI-ready insights: qualitative analysis of menstrual health

This post explains how AI-enabled qualitative research can make the findings from a PLOS One qualitative study actionable for researchers, NGOs, and WASH program teams. The primary keyword "qualitative analysis of menstrual health" guides practical steps: from extracting themes and quotes to quantifying patterns across participant segments. According to PLOS One (published July 24, 2026), the Khulna Railway Slum study recorded 18 in-depth interviews and 5 key informant interviews between 01/09/2025 and 31/10/2025, producing rich, policy-relevant themes about water, sanitation, disposal, stigma, and gendered access to products.

Key Takeaways

According to PLOS One (published July 24, 2026), women and adolescent girls in Khulna’s Railway Slum experience menstruation as a socially and structurally shaped burden driven by inadequate WASH, stigma, and economic constraints.

PLOS One (24 July 2026) reports 18 in-depth interviews and 5 key informant interviews collected between 01/09/2025 and 31/10/2025.
PLOS One (24 July 2026) finds three dominant domains shaping experience: structural-environmental constraints, socio-cultural stigma, and gendered inequities in access to menstrual resources.
PLOS One (24 July 2026) reports that 72% of participants had lived in the slum for over eight years, amplifying chronic exposure to poor sanitation and limited water.
Direct quotations from participants illuminate mechanisms: "When water stops coming from the tap, I wait for hours. If it doesn’t come, I can’t clean myself properly" (IDI-08, PLOS One).

What Happened: study design and measurements

Answer: the PLOS One study used in-depth qualitative methods to map lived menstrual experiences in one Khulna informal settlement and to surface actionable themes.

According to PLOS One (published July 24, 2026), researchers conducted 18 IDIs with women and adolescent girls aged 15–45 and 5 KIIs with community health workers and NGO staff between 01/09/2025 and 31/10/2025.

According to PLOS One, data collection used semi-structured interviews in Bengali, audio recordings, and thematic analysis with a combined deductive-inductive codebook guided by Feminist Political Ecology; NVivo v12 supported coding.

According to PLOS One, ethical approval was granted by Gopalganj Science and Technology University IRB (Approval No. 473478-FY 2024–2025; Approval Date: 10 August 2025).

Findings snapshot

Date / Source	Metric	Value	Implication
01/09/2025–31/10/2025, PLOS One	In-depth interviews	18 participants aged 15–45	Rich firsthand narratives suitable for thematic and quote extraction
01/09/2025–31/10/2025, PLOS One	Key informant interviews	5 stakeholders	Triangulation with NGO and school perspectives
24/07/2026, PLOS One	Dominant themes	3 domains: WASH constraints, stigma, gendered inequity	Interventions must combine infrastructure, education, and economic access
24/07/2026, PLOS One	Residency duration	72% lived in the slum >8 years	Long-term exposure indicates structural, not episodic, problems

Implications for researchers and program teams

How should qualitative researchers prioritize analysis?

Answer: prioritize cross-cutting codes that link environment, stigma, and resource access to identify leverage points for intervention.

According to PLOS One (published July 24, 2026), the three conceptual domains can be mapped to program levers: WASH upgrades, community education, and subsidized product distribution.

Researchers should extract verbatim quotes tied to each domain (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)) to illustrate lived mechanisms in reports and grant proposals.

What should NGOs and WASH teams change in monitoring?

Answer: monitor menstrual dignity indicators alongside standard WASH metrics, and collect disaggregated qualitative feedback monthly.

According to PLOS One, women reported lack of privacy, water scarcity, and unsafe disposal practices; programs should add indicators for privacy (locks, lighting), continuous water availability, and menstrual waste options.

Programs can triangulate short IDI extracts with routine service metrics to detect seasonal or acute failures (the PLOS One sample collected data before the 2026 monsoon vulnerabilities were explicitly discussed).

How Evidano helps: AI mappings from problem to feature

Problem: dispersed qualitative data slows synthesis → Solution: thematic synthesis at scale

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

According to the PLOS One methods (24 July 2026), manual coding used NVivo v12 and independent coders to reach consensus; Evidano accelerates the same steps by ingesting transcripts and producing reproducible thematic maps with source-linked quotations.

Feature mapping: upload transcripts, run automated thematic extraction, confirm and refine codes, and export a codebook and frequency tables for donor reports.

Problem: finding representative quotes is time-consuming → Solution: quote search and tagging

Answer: Evidano can surface participant quotes tied to codes and segments so teams can quote ethically and quickly.

According to PLOS One, participant quotes such as "When water stops coming from the tap, I wait for hours" (IDI-08) are central evidence; Evidano can index quotes by code, participant profile, and date to create shareable evidence packets.

Relevant Evidano features: transcription with PII redaction, code→subcode hierarchy, and exportable quote banks documented for ethics.

Problem: multilingual or audio data increases overhead → Solution: transcription and translation

Answer: use integrated speech-to-text and translation to convert Bengali audio into coded English transcripts ready for cross-study comparison.

According to PLOS One, interviews were conducted in Bengali and then translated; Evidano’s speech-to-text and translation tools can standardize that process while preserving custom dictionaries and consent notes.

Feature mapping: audio ingestion → automated transcript → human review → AI-assisted coding saves weeks on multi-language projects.

Problem: data security and ethical limits on sharing → Solution: privacy-first workflows

Answer: Evidano supports encrypted storage and controlled access so teams can analyze sensitive menstrual data while preserving participant confidentiality.

Evidano keeps data encrypted and does not use customer data to train third-party models, which supports the IRB restrictions reported by the PLOS One team (Gopalganj Science and Technology University IRB, Approval Date: 10 August 2025).

Teams can produce de-identified thematic outputs and export minimal datasets (quote banks, codebook, frequency tables) for ethical sharing in line with the source study.

Try Evidano features and security

Answer: See product capabilities and privacy documentation before piloting an analysis workflow.

Explore Evidano’s features page for thematic analysis, AI chat over documents, and visualization options that map directly onto the PLOS One analytic steps.

For teams starting with audio, visit Evidano’s speech-to-text page to assess transcription accuracy and PII redaction prior to upload.

FAQ: qualitative analysis of menstrual health

How many interviews are enough for a thematic study like the PLOS One Khulna paper?

Answer: aim for saturation rather than a fixed number; the PLOS One study reached saturation at 18 IDIs and 5 KIIs.

According to PLOS One (data collected 01/09/2025–31/10/2025), the research team stopped at 18 IDIs because no new conceptual codes emerged after the fifteenth interview and three additional interviews confirmed saturation.

Can AI tools preserve ethical constraints on sensitive menstrual transcripts?

Answer: yes, if the platform supports PII redaction, encrypted storage, and access controls.

According to the PLOS One ethics statement (IRB Approval Date: 10 August 2025), full transcripts were withheld for confidentiality; AI workflows should mirror that practice by producing de-identified outputs and controlled minimal datasets for sharing.

What immediate indicators should programs add to monitoring after reading PLOS One?

Answer: add menstrual dignity indicators for privacy, continuous water availability, and disposal options.

According to PLOS One, lack of locks, intermittent water, and no disposal bins were repeatedly cited; track these monthly alongside qualitative feedback to catch recurring barriers.

How do you convert qualitative themes into quantifiable program metrics?

Answer: code frequency, segment by age/marital status, and convert occurrence rates into dashboard indicators.

According to PLOS One, participants were stratified by age and residency duration; use those segments to count mentions of specific barriers and present them as percentages in reports.

Conclusion & Next Steps

Answer: AI-enabled qualitative analysis turns the PLOS One Khulna study from narrative evidence into targeted program actions on WASH, education, and product access.

According to PLOS One (published July 24, 2026), the study’s themes directly map to practical interventions that NGOs and municipal actors can implement and monitor.

Next steps for research teams: ingest transcripts and KIIs, run thematic extraction, validate codes with local stakeholders, and produce a minimal de-identified dataset for policymakers.

Ready to pilot this workflow? Try Evidano for free.

Topics

qualitative analysis of menstrual health
AI qualitative research
menstrual hygiene qualitative study
menstrual health in slums

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AI-ready insights: qualitative analysis of menstrual health

Key Takeaways

What Happened: study design and measurements

Findings snapshot

Implications for researchers and program teams

How should qualitative researchers prioritize analysis?

What should NGOs and WASH teams change in monitoring?

How Evidano helps: AI mappings from problem to feature

Problem: dispersed qualitative data slows synthesis → Solution: thematic synthesis at scale

Problem: finding representative quotes is time-consuming → Solution: quote search and tagging

Problem: multilingual or audio data increases overhead → Solution: transcription and translation

Problem: data security and ethical limits on sharing → Solution: privacy-first workflows

Try Evidano features and security

FAQ: qualitative analysis of menstrual health

How many interviews are enough for a thematic study like the PLOS One Khulna paper?

Can AI tools preserve ethical constraints on sensitive menstrual transcripts?

What immediate indicators should programs add to monitoring after reading PLOS One?

How do you convert qualitative themes into quantifiable program metrics?

Conclusion & Next Steps

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