This post explains how AI-enabled qualitative analysis can turn interview transcripts into clear, actionable evidence for researchers, NGOs, and policy teams working on menstrual health. The primary keyword is qualitative analysis of menstrual health and we use the PLOS ONE study by Alam and Al-Mamun (published July 24, 2026) as a worked example to show what to extract, how to validate themes, and what decisions follow. According to the PLOS ONE article, data were collected in September–October 2025 via 18 in-depth interviews and five key informant interviews in the Khulna Railway Slum; those exact numbers make the study a concise dataset for thematic synthesis and cross-segment comparison.
Key Takeaways
The PLOS ONE study PLOS ONE published July 24, 2026, documents how structural WASH gaps, socio-cultural stigma, and gendered resource inequities shape menstrual experiences in Khulna’s Railway Slum. The PLOS ONE study used 18 in-depth interviews and five key informant interviews collected in September–October 2025 to build three thematic domains that explain everyday period management and its harms. These findings show concrete intervention points for sanitation upgrades, education, and product access.
- 18 in-depth interviews and 5 key informant interviews were conducted between September 1 and October 31, 2025, according to the PLOS ONE study.
- The PLOS ONE article (published July 24, 2026) reports that 39% of participants were aged 20–30 and 67% were married, offering demographic context that supports age- and household-focused program design.
- Participants described concrete risks: water scarcity, lack of private toilets, and unsafe disposal; one respondent 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) in the PLOS ONE interview data.
- The PLOS ONE authors conclude that integrated WASH, education, and subsidized product distribution are needed to address menstrual dignity in Khulna’s informal settlements.
What happened and how the study was done
Answer: A focused qualitative study in Khulna’s Railway Slum documented lived menstrual experiences and identified three interacting domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities in resource access.
According to the PLOS ONE study by Alam and Al-Mamun (published July 24, 2026), researchers collected data between September and October 2025 using 18 in-depth interviews (IDIs) with women aged 15–45 and five key informant interviews (KIIs) with NGO workers, teachers, and community health staff, then applied a mixed inductive-deductive thematic analysis guided by Feminist Political Ecology.
The PLOS ONE authors report participant characteristics: ages 15–45, 39% non-literate or minimal schooling, 33% with secondary education, 28% primary education, 72% resident in slums for over eight years, and occupation mix (28% homemakers, 17% domestic/textile workers, 11% small sellers, 22% students). The study used NVivo v12 for coding and achieved thematic saturation by the fifteenth interview, as the authors state.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| Sept–Oct 2025 | In-depth interviews (IDIs) | 18 | Sufficient for thematic depth and saturation in this focused slum study |
| Sept–Oct 2025 | Key informant interviews (KIIs) | 5 | Provided triangulation with NGO and teacher perspectives |
| July 24, 2026 | Publication date | PLOS ONE article (Alam & Al-Mamun, 2026) | Peer-reviewed, open access source for program design |
| Participant demographics (reported in study) | Percent married | 67% | Household decision-making and male-controlled budgets influence product access |
| Participant demographics (reported in study) | Age 20–30 share | 39% | Target age range for adult-focused interventions and livelihoods-linked solutions |
Implications for researchers and program teams
Answer: The PLOS ONE evidence implies that mixed interventions are necessary: upgrade gender-responsive WASH, integrate menstrual education, and subsidize product access in slums.
For qualitative researchers: the PLOS ONE study demonstrates that a small, well-coded dataset (18 IDIs, 5 KIIs) collected over two months can yield transferable themes when analysis combines inductive and deductive coding and reports saturation transparently.
For NGO program designers: the PLOS ONE findings (published July 24, 2026) highlight three program levers (continuous water supply, private toilets with lighting and locks, and discreet disposal options) to reduce absenteeism and improve dignity.
For policy teams: the PLOS ONE authors recommend embedding menstrual health into WASH and slum-upgrading plans rather than running isolated product giveaways, because infrastructure and stigma sustain period poverty.
How Evidano helps with qualitative analysis of menstrual health
Problem: small qualitative datasets are rich but hard to scale
Answer: A focused dataset like the PLOS ONE study (18 IDIs, 5 KIIs) needs reproducible coding, versioned translations, and extractable quotes for policy briefs.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. Evidano can ingest transcripts (Bengali and English), apply consistent coding, and export theme frequency tables and verbatim quotes linked to participant codes.
Problem: preserving translation fidelity and participant confidentiality
Answer: Translation and redaction are recurring bottlenecks in cross-language MHH research.
According to the PLOS ONE methods, interviews were conducted in Bengali and translated to English with back-checking; Evidano supports translation with a custom dictionary and PII redaction to reproduce that workflow and output the same quote strings and source codes for audit trails. See the Evidano translation and data security features for details.
Problem: turning themes into stakeholder-ready evidence
Answer: Programs need tables, quotations, and cross-segment frequency counts to make budgets and designs persuasive.
Evidano automates thematic frequency analysis, generates co-occurrence networks, and produces exportable tables of themes by age, marital status, or occupation, matching the PLOS ONE study’s segmentation (ages 15–45, marital status, occupation). Learn more on Evidano features.
FAQ: qualitative analysis of menstrual health
What is the sample size needed for a qualitative analysis of menstrual health?
Answer: There is no fixed number; the PLOS ONE study reached thematic saturation with 18 IDIs and 5 KIIs collected in September–October 2025.
The PLOS ONE authors explain that saturation was reached by the 15th IDI and confirmed by three additional interviews, illustrating that adequacy depends on richness of responses, topic sensitivity, and heterogeneity of participant backgrounds.
How do you ensure translation accuracy in cross-language qualitative datasets?
Answer: Use paired translation with back-checking and independent reviewers as the PLOS ONE study did for Bengali-to-English transcripts.
The PLOS ONE methods describe a two-step verification: initial translation by the researchers followed by cross-checking by a bilingual team member. Platforms that support custom translation glossaries reduce semantic drift when automating this step.
Can AI safely analyze sensitive menstrual health interviews?
Answer: Yes, when data handling includes PII redaction, encrypted storage, and clear IRB-aligned export controls.
The PLOS ONE dataset was ethically restricted for confidentiality; the authors stored recordings and transcripts securely and required IRB oversight for data access. Evidano provides PII redaction and encrypted storage workflows to mirror those safeguards; see Evidano data security.
Which outputs make qualitative findings actionable for WASH programs?
Answer: Frequency-by-segment tables, verbatim quotations for advocacy, and mapped infrastructure gaps tied to demographics.
The PLOS ONE study produced themes tied to water access, privacy, disposal, and stigma; turning those into budget-ready items means quantifying theme prevalence (for example, percent of interviews reporting water scarcity) and supplying 2-3 representative quotes per theme for funder briefs.
Conclusion & Next Steps
Answer: AI-enabled qualitative analysis converts focused studies like the PLOS ONE Khulna research into program-ready evidence by standardizing coding, preserving translations, and producing cross-segment theme counts and quote libraries.
The PLOS ONE study (published July 24, 2026) shows that a small, ethically managed interview dataset can guide infrastructure, education, and subsidy interventions when analysis is rigorous and reproducible.
If you collect transcripts, open-ended survey responses, or field notes and want a validated pipeline from raw text to stakeholder-ready tables and quotes, Evidano can reproduce the PLOS ONE analytic workflow and scale it across sites. Try the platform and see how a two-month field dataset becomes actionable evidence: Try Evidano for free.
Ethics note: qualitative findings about health are for research and program design, not clinical diagnosis; always follow IRB and local ethical guidance when sharing sensitive transcripts.