AI Qualitative Analysis: Menstrual Health Insights

According to the PLOS One study published July 24, 2026, 18 in-depth interviews and 5 key informant interviews collected between 01/09/2025 and 31/10/2025 reveal that menstrual experiences in Khulna slums are driven by structural-environmental constraints, socio-cultural stigma, and gendered inequities in resource access. PLOS One

• 18 in-depth interviews and 5 key informant interviews were conducted between 01 September 2025 and 31 October 2025, per the PLOS One study published July 24, 2026.
• Participant demographics in the PLOS One study included 39% aged 20–30 years and 67% married, and the authors report 39% had minimal or no schooling, 33% had secondary education, and 28% had primary education.
• According to the PLOS One findings published July 24, 2026, 72% of participants had lived in the slum for over eight years, reinforcing long-term exposure to WASH deficits and overcrowding.
• Multiple participants told the research team in quotes such as, “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 infrastructure and stigma combine to reduce menstrual dignity.

What happened: the PLOS One qualitative study (Alam & Al-Mamun) conducted 18 in-depth interviews and 5 key informant interviews in the Khulna Railway Slum between 01 September 2025 and 31 October 2025 to explore lived menstrual experiences. According to the PLOS One article published July 24, 2026, the authors used semi-structured interviews in Bengali, transcribed and translated the data, and applied a combined inductive-deductive thematic analysis guided by Feminist Political Ecology.

According to the PLOS One study published July 24, 2026, the authors reached thematic saturation by the 15th interview and analyzed data with NVivo v.12, triangulating IDIs and KIIs and following COREQ reporting standards.

According to the PLOS One findings published July 24, 2026, three mutually reinforcing conceptual domains structured the results: (1) structural-environmental constraints (water, sanitation, waste disposal, overcrowding), (2) socio-cultural stigma (shame, silence, restricted mobility), and (3) gendered inequities in access to menstrual resources (cost, household decision-making).

Implication: qualitative teams should combine thematic depth with frequency and segment analyses to inform multi-component interventions. According to the PLOS One study published July 24, 2026, the layered problems in Khulna require WASH upgrades, stigma reduction, and subsidized product access rather than single-focus programs.

• Design: Use sample stratification to compare adolescents (15–19) versus adult women (20–45), as the PLOS One study did, because 39% of participants were in their 20s and adolescents reported distinct school absenteeism drivers.
• Monitoring: Track code frequency and co-occurrence (for example, 'water scarcity' + 'shame') monthly to measure whether WASH investments reduce stigma-linked absenteeism, following the PLOS One emphasis on structural-social interaction.
• Reporting: Include verbatim quotes to preserve voice; the PLOS One paper uses quotations such as “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, student) to illustrate lived risk.

According to the PLOS One study published July 24, 2026, menstrual health in Khulna’s informal settlements is shaped by water scarcity, inadequate sanitation, disposal gaps, stigma, and constrained household budgets; addressing these issues requires integrated WASH, education, and product-access interventions.

Qualitative researchers and program teams can replicate the study’s rigor faster by using AI-enabled platforms to transcribe in local languages, maintain translation checks, perform thematic plus cross-segment analyses, and export IRB-friendly minimal datasets.

If you want to convert interview transcripts into program-ready evidence quickly, Try Evidano for free. For platform details see Evidano features.