Qualitative analysis of gender-responsive food systems

Evidano is an AI-powered qualitative data analysis platform that can reproduce the study’s mixed-methods pipeline, turning interviews, FGDs and surveys into segment-aware evidence in days.

The PLOS Soc Trang study (Published 1 July 2026) found a policy-to-practice gap: unstable, project-based funding, information and training flowing primarily to men, and social norms limiting women’s participation.

• 67% of key informants (16/24) said budgets are spread too thin, showing fragmented funding undermines scale and continuity.
• 92% of households reported that women benefit from programs, but many benefits are shallow and uneven.
• 83% of households report men are the primary recipients of training, constraining women’s uptake of technical practices.
• Leadership is skewed: 66.7% of positions are held by men and only 8.3% by women, indicating formal decision-making power is concentrated.

The study found a consistent mismatch between national gender-mainstreaming policies and local implementation in Soc Trang Province.

The authors document that although policies and pilots exist (rice, aquaculture, forestry), funding is unstable and mostly project-based; information and technical training flow primarily to men; social norms limit women’s participation in technical trainings; and local implementers often lack resources to tailor programs.

The paper combined policy review, NVivo-coded interviews, 6 FGDs, and household surveys to triangulate these patterns.

• Funding is fragmented: 67% (16/24) key informants said budgets are spread too thin.
• Women reported higher difficulty accessing scheme paperwork (80% women vs 40% men in one commune).
• Local cooperatives that subsidize 50% of input costs (Long Duc) improved uptake and perception.

Researchers, UX teams, and policy analysts can use mixed qualitative data to expose implementation barriers that policy texts do not reveal.

If your job is to produce credible, stakeholder-ready evidence from interviews and surveys, this study is a clear example of how mixed qualitative data exposes implementation barriers beyond policy text.

• Design better instruments: disaggregate transcripts by gender, commune and role to surface patterns, for example who attends training and who controls decisions.
• Quantify qualitative signals: convert NVivo-style codes into frequencies and cross-segment comparisons, for example percent of women vs men reporting access barriers.
• Prioritize local narratives: include quote-level evidence tied to demographics so policymakers hear which measures work (cooperatives, subsidies) and which don’t (one-off trainings).

These seven steps reproduce the paper’s mixed-methods pipeline quickly.

Step 1: Gather inputs, policy docs, interview audio/transcripts, FGDs, survey spreadsheet.

• Step 2: Upload to Evidano and run transcription and glossary-based translation.
• Step 3: Import or auto-generate a codebook; label 50–100 segments to seed AI-assisted coding.
• Step 4: Run thematic extraction and frequency analysis by segment (gender, commune, role).
• Step 5: Produce visuals, co-occurrence network, code hierarchy, and quote matrices.
• Step 6: Draft a 1-page policy brief with evidence-backed recommendations, for example fund cooperatives, make training accessible to women, monitor gendered outcomes.
• Step 7: Design a short follow-up FGD or AI-avatar interview to validate counterintuitive findings.

The PLOS Soc Trang study shows that policy commitments alone do not equal gender-equitable outcomes.

If you need to turn interviews, FGDs and surveys into defensible, segment-aware evidence faster, you can reproduce this mixed-methods triangulation using AI-enabled qualitative workflows.

• Start by re-creating the paper’s inputs in Evidano: upload transcripts and surveys, run thematic and cross-segment analyses, and export policy-ready visuals.
• Ready to try it? Try Evidano for free or visit Evidano to explore a hands-on demo and import your corpus.