Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. Qualitative researchers studying menstrual health need reproducible thematic synthesis that preserves context, quotes, and timeline; the primary keyword for this post is qualitative analysis menstrual health. The PLOS One study of the Khulna Railway Slum (published 24 July 2026) used 18 in-depth interviews and 5 key informant interviews collected between 01 September 2025 and 31 October 2025 to map how infrastructure, stigma, and gender shape menstrual hygiene. This post shows how AI-enabled qualitative research workflows speed coding, extract verbatim quotes, and produce cross-segment summaries suitable for program design and policy. The examples and stats below come directly from the PLOS One article.
Key Takeaways
According to the PLOS One study published on 24 July 2026, menstrual experiences in Khulna Railway Slum are shaped by three interacting domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities in resource access. The PLOS One study collected 18 in-depth interviews and 5 key informant interviews between 01 September 2025 and 31 October 2025 to reach thematic saturation.
- 18 in-depth interviews and 5 KIIs were conducted between 01/09/2025 and 31/10/2025, according to PLOS One (published 24 July 2026).
- 72% of participants had lived in the slum for over eight years and 39% were aged 20–30, as reported in the PLOS One participant table.
- The PLOS One team used Braun and Clarke’s thematic framework and NVivo v.12 for coding and achieved saturation after 15 IDIs, per the Methods (data collection completed 31 October 2025).
- Direct participant testimony in PLOS One underlines water scarcity and privacy as immediate barriers: “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).
What happened and how the study was done
Answer: The PLOS One study (published 24 July 2026) used in-depth qualitative methods to map lived menstrual experiences in Khulna Railway Slum.
The PLOS One research team collected 18 in-depth interviews with women and adolescent girls aged 15–45 years and 5 key informant interviews, with fieldwork conducted from 01 September 2025 to 31 October 2025.
The PLOS One authors applied a Feminist Political Ecology framework and combined deductive and inductive thematic analysis following Braun and Clarke, coding in NVivo v.12, as described in the Methods section of the PLOS One article.
The PLOS One findings cluster around three domains: (1) structural-environmental constraints such as inadequate WASH and disposal, (2) socio-cultural stigma including silence and restricted mobility, and (3) gendered inequities that limit access to menstrual products and information.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 24 July 2026 | Publication | PLOS One | Peer-reviewed qualitative report for MHH in Khulna slums |
| 01 Sep 2025 – 31 Oct 2025 | Interviews | 18 IDIs, 5 KIIs | In-depth narratives plus stakeholder perspectives |
| Participant demographics | Age range | 15–45 years (39% aged 20–30) | Covers adolescents and adult women for comparative themes |
| Residence duration | Proportion | 72% lived in the slum >8 years | Long-term exposure to structural constraints shapes practices |
| Common themes | Top domains | WASH gaps, stigma, period poverty | Points to combined infrastructure and social interventions |
Implications for qualitative researchers: qualitative analysis menstrual health
Answer: Researchers should treat menstrual health as a socio-ecological issue that combines infrastructural, cultural, and gendered dimensions, as the PLOS One study (24 July 2026) demonstrates.
The PLOS One evidence shows that WASH failures interact with stigma to produce health and dignity harms; qualitative research protocols should therefore include household-level observation, privacy-mapping, and stakeholder KIIs to capture systems-level barriers.
The PLOS One Methods show thematic saturation was reached after 15 IDIs; researchers planning similar studies should budget for 15–25 IDIs plus 3–6 KIIs depending on subgroups and contextual heterogeneity.
The PLOS One participant quotations demonstrate the analytic value of verbatim text: researchers should preserve raw quotes and link them to participant attributes (age, marital status, residence duration) for cross-segment analysis.
How Evidano helps
Problem: Slow synthesis of interview data → Solution: Rapid thematic coding
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano accelerates codebook generation by suggesting initial codes from transcripts and iteratively refining them with human-in-the-loop review, reducing the time needed to reach thematic saturation described in the PLOS One Methods.
Evidano integrates with transcription workflows to convert audio into time-stamped text and supports custom dictionaries to preserve local terms from Khulna-style interviews.
Problem: Quote extraction and attribution → Solution: Verbatim quote linking
Evidano preserves verbatim quotes with participant metadata so analysts can extract quotations like "When water stops coming from the tap, I wait for hours" (IDI-08) and trace them to participant age, residence duration, and interview date.
Evidano’s searchable quote library and co-occurrence visualizations make it faster to produce evidence-based policy briefs that respond to the WASH and stigma themes PLOS One identified.
Problem: Cross-segment comparison is time-consuming → Solution: Cross-segment and frequency analysis
Evidano generates frequency matrices and cross-tabulations across attributes (age group, marital status, years in settlement) so teams can replicate PLOS One style comparisons (for example, adolescent vs adult coping strategies) without manual spreadsheets.
For teams preparing mixed interventions, the Evidano Features page outlines automated thematic, frequency, and cross-segment reports that support program design.
Problem: Reporting to stakeholders → Solution: Visualizations and AI chat
Evidano creates word clouds, co-occurrence networks, and hierarchical code→subcode visuals that make findings from studies like PLOS One immediately actionable for WASH planners and NGOs.
Evidano’s AI chat over your documents lets non-technical stakeholders ask focused questions (for example, “Which quotes link water scarcity to school absenteeism? ”) and receive extracted evidence with source attributions.
FAQ: qualitative analysis menstrual health
How were themes derived in the PLOS One study?
Answer: The PLOS One team used a combined deductive and inductive thematic approach guided by Feminist Political Ecology and Braun and Clarke’s six-phase framework.
The PLOS One Methods report that initial coding used three conceptual domains (structural-environmental constraints, socio-cultural stigma, gendered inequities) and incorporated emergent codes from 18 IDIs and 5 KIIs collected between 01 September 2025 and 31 October 2025.
What are the concrete statistics I can cite from the PLOS One paper?
Answer: You can cite that the PLOS One study conducted 18 in-depth interviews and 5 key informant interviews, and that data collection occurred between 01/09/2025 and 31/10/2025.
The PLOS One participant table also reports that 72% of respondents had lived in the slum for over eight years and that 39% of participants were aged 20–30, details that support contextual claims about long-term exposure to infrastructural deficits.
Can AI safely process sensitive menstrual transcripts?
Answer: Yes, AI platforms can process sensitive transcripts when they include PII redaction, encryption, and ethics-aware workflows.
Evidano’s platform includes transcription with PII redaction and secure storage options; researchers must still follow IRB rules and the PLOS One study’s ethical practice of replacing names with pseudonyms, as described in the PLOS One Ethics section (IRB Approval No. 473478-FY 2024–2025; Approval Date: 10 August 2025).
What immediate steps should a qualitative team take after reading the PLOS One paper?
Answer: Teams should map data needs to program objectives, collect participant metadata, and plan for privacy-preserving transcription and coding.
Following the PLOS One recommendations, teams should include WASH facility audits, routine quote preservation, and stakeholder KIIs; using AI-assisted tools can reduce manual workload and speed policy-ready synthesis.
Conclusion & Next Steps
The PLOS One study (published 24 July 2026) documents how inadequate WASH, pervasive stigma, and period poverty shape menstrual experiences in Khulna Railway Slum using 18 IDIs and 5 KIIs collected in Sep–Oct 2025.
Qualitative analysis menstrual health benefits from reproducible, auditable workflows that preserve quotes, attributes, and timeline so findings can inform gender-responsive WASH and education programs.
If your team needs reproducible thematic coding, quote extraction, and cross-segment reporting for MHH research, consider integrating Evidano into your workflow and testing the end-to-end process.
Get started by Try Evidano for free to import transcripts, run AI-assisted thematic analysis, and produce stakeholder-ready visualizations.