AI-assisted insights: AI qualitative analysis menstrual health

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The primary keyword for this post is "AI qualitative analysis menstrual health, " which frames practical guidance for qualitative researchers, NGOs, and WASH program teams. This post refracts the July 24, 2026 PLOS One qualitative study of Khulna Railway Slum through an AI-enabled research workflow and gives concrete steps to extract robust thematic evidence from small, ethically sensitive interview datasets.

Key Takeaways

According to PLOS One, the 2026 qualitative study of Khulna Railway Slum finds that menstruation is shaped by three linked domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities.

Data were collected between 01/09/2025 and 31/10/2025 and the study conducted 18 in-depth interviews (IDIs) plus 5 key informant interviews (KIIs), according to PLOS One.
The study, published on July 24, 2026 in PLOS One, reports that Khulna district has roughly 2.61 million people and about 20% live in slums, intensifying WASH vulnerabilities.
Participant demographics reported by PLOS One include ages 15–45, with 39% of respondents in the 20–30 range and 67% married, data that shape subgroup coding and interpretation.
Direct lived-experience quotes in PLOS One capture water scarcity and shame, for example, “When water stops coming from the tap, I wait for hours, ” (IDI-08, Housewife).

What happened and how the study worked

Answer: The PLOS One article reports a focused qualitative study in Khulna Railway Slum conducted between 01/09/2025 and 31/10/2025 using in-depth interviews and key informant interviews.

According to PLOS One, researchers used 18 IDIs with women and girls aged 15–45 and 5 KIIs to reach thematic saturation, with interviews averaging 25–30 minutes.

According to PLOS One, the analytic approach combined deductive coding from a Feminist Political Ecology framework and inductive codes using Braun and Clarke’s six-phase thematic analysis implemented in NVivo v.12.

According to PLOS One, ethical safeguards included IRB approval on 10 August 2025 (Approval No. 473478-FY 2024–2025), written consent, pseudonymization, and restricted transcript access because of confidentiality concerns.

Findings snapshot

Date / Source	Metric	Value (from PLOS One)	Implication for qualitative research
01/09/2025–31/10/2025, PLOS One	Interviews	18 IDIs; 5 KIIs	Small, dense datasets require careful saturation checks and transparent codebooks.
Published 24/07/2026, PLOS One	Participants’ age range	15–45 years (39% aged 20–30)	Stratify codes by adolescent vs adult experiences when comparing themes.
2026, PLOS One	Local context	Khulna district ~2.61M population; ~20% in slums; ~520 informal settlements	Contextualize findings to urban informal-settlement WASH constraints when generalizing.

Implications for qualitative researchers and WASH teams

Answer: The PLOS One study implies that researchers must combine environmental, social, and gender lenses when analyzing menstruation narratives.

According to PLOS One, infrastructural constraints such as shared toilets, scarce water, and unsafe disposal shape both the content and silence in interviews, so researchers should code for material constraints and stigma separately.

According to PLOS One, adolescent participants reported school absenteeism tied to inadequate facilities, so program evaluators should pair qualitative themes with attendance or service-use metrics when possible.

According to PLOS One, reflexivity and triangulation (IDIs + KIIs) strengthened trustworthiness; replicate these methods and document codebook evolution for auditability.

How Evidano helps

Problem: transcribing and protecting sensitive Bengali interviews → Solution

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

Evidano’s transcription pipeline supports custom dictionaries and PII redaction, which addresses the PLOS One concern that full transcripts cannot be publicly shared for confidentiality.

Use Evidano Speech-to-Text to transcribe Bengali audio, apply a custom lexicon for local terms, and automatically redact participant identifiers before coding.

Problem: synthesizing small-N thematic data across subgroups → Solution

Answer: Evidano accelerates thematic, frequency, and cross-segment analysis so teams can compare adolescents versus adults quickly.

Evidano’s thematic and cross-segment tools let researchers tag 18 IDIs and 5 KIIs, run co-occurrence and hierarchical code queries, and export a reproducible codebook, matching the rigor called for in the PLOS One methods section.

See Evidano features for visualizations such as co-occurrence networks that make structural-environmental, stigma, and gendered-inequity themes explicit.

Problem: producing policy-ready summaries while preserving ethics → Solution

Answer: Evidano produces extractable quotes and frequency tables while preserving anonymization and an audit trail.

Evidano’s secure storage and configurable access controls help teams meet the ethical restrictions described in PLOS One, enabling sharing of de-identified excerpts and the minimal dataset without exposing raw transcripts.

For details, review Evidano’s data practices at Evidano data security.

FAQ: AI qualitative analysis menstrual health

How can AI help analyze qualitative interviews about menstrual health?

Answer: AI can speed transcription, surface themes, and quantify co-occurrence patterns while preserving human-led interpretation.

According to PLOS One, small qualitative datasets (18 IDIs, 5 KIIs) depend on careful thematic coding; AI helps by suggesting initial codes and summarizing frequent themes while researchers validate and refine the codebook.

Can AI identify stigma-related language in interviews?

Answer: Yes, AI tools can flag language associated with shame, concealment, and restricted mobility, but researchers must verify context.

According to PLOS One, participants used terms and metaphors like _lojja-r bishoy_ and described behaviors such as hiding cloths; AI can surface these patterns but human coders should confirm interpretive meaning.

How do I preserve participant confidentiality when using AI?

Answer: Apply PII redaction, restrict model access, and keep an audit trail for all outputs.

According to the ethical procedures reported in PLOS One, IRB-approved studies may limit transcript sharing; use platforms with redaction and secure storage to meet similar requirements.

What sample size is appropriate for thematic saturation in menstrual health studies?

Answer: Saturation depends on heterogeneity, but the PLOS One study reached saturation with 18 IDIs and 5 KIIs in that context.

According to PLOS One, the researchers determined saturation by iterative coding and stopped after three additional interviews confirmed no new themes.

Conclusion & Next Steps

Answer: The PLOS One study shows that menstrual experiences in Khulna slums are shaped by material constraints, stigma, and gendered resource gaps, and AI-enabled qualitative workflows can make that evidence more actionable.

According to PLOS One, targeted upgrades to gender-responsive WASH, community education, and subsidized products are policy priorities; researchers can support these actions by producing transparent thematic evidence and de-identified excerpts.

Next step for teams: pilot an AI-enhanced workflow on a subset of interviews, validate code suggestions manually, and share de-identified thematic summaries with stakeholders.

Get started by exploring how Evidano’s transcription and thematic tools fit your project and Try Evidano for free.

Topics

AI qualitative analysis menstrual health
qualitative analysis menstrual health
AI-assisted thematic analysis
menstrual health qualitative research

Keep reading

Browse all articles

AI-assisted insights: AI qualitative analysis menstrual health

Key Takeaways

What happened and how the study worked

Findings snapshot

Implications for qualitative researchers and WASH teams

How Evidano helps

Problem: transcribing and protecting sensitive Bengali interviews → Solution

Problem: synthesizing small-N thematic data across subgroups → Solution

Problem: producing policy-ready summaries while preserving ethics → Solution

FAQ: AI qualitative analysis menstrual health

How can AI help analyze qualitative interviews about menstrual health?

Can AI identify stigma-related language in interviews?

How do I preserve participant confidentiality when using AI?

What sample size is appropriate for thematic saturation in menstrual health studies?

Conclusion & Next Steps

Topics

Keep reading

Company

About

Preview Evidano

Account

Newsletter