AI Qualitative Analysis: Gender & Low-Emission Food Systems

A mixed-methods case study in Soc Trang Province finds that gender-aware low-emission food system policies are mainstreamed on paper but implementation is undermined by unstable funding, limited training access for women, and rigid top-down rollout. AI-assisted qualitative workflows can reproduce and quantify these local, gendered patterns from interviews and FGDs, speeding reproducible insights for program design.

• Primary source: PLOS Sustainable Transformation, Pham et al., published 1 July 2026.
• Fieldwork and sample: fieldwork took place May–October 2024, including 24 key informant interviews (12 men, 12 women), 24 household surveys, and six FGDs.
• Major barriers: unstable funding, limited training access for women, and weak monitoring for gender outcomes undermined local implementation.
• Reproducible pipeline: the study used transcription, NVivo-assisted coding, thematic analysis, and cross-segment frequency checks, a workflow you can reproduce in a 2-week Evidano pilot.

A quick summary: a mixed-methods case study in Soc Trang Province finds that although Vietnam has mainstreamed gender in low-emission food system policies, implementation (May–Oct 2024 fieldwork) is undermined by unstable funding, limited training access for women, and rigid top-down rollout.

• Primary source: PLOS Sustainable Transformation (Pham et al., published 1 July 2026).
• Why it matters for researchers and program teams: qualitative nuance (quotes, local norms) explains why policy alone does not shift practice, and those qualitative signals are reproducible with AI-assisted workflows.

The methods answer the question of how evidence was gathered and coded: researchers combined a policy review, 24 key informant interviews, 24 household surveys, and six FGDs in two communes of Soc Trang and used transcription and NVivo-assisted open coding to produce themes. Interview and FGD audio were transcribed, open coding produced keywords that were grouped into themes via thematic analysis, and NVivo was used to organise codes and calculate descriptive frequencies.

• Triangulation: qualitative themes were cross-checked with survey frequencies to validate patterns (for example, 92% of households reported that women benefit from government programs, but only 16.7% said women access training).
• Local nuance: gender roles and cooperative support in Long Duc versus information gaps in An Thanh 3 shaped differential adoption of AWD and other low-emission practices.

This checklist answers how to reproduce the study pipeline in a condensed pilot: follow these six steps to reproduce transcription, coding, analysis, and reporting in Evidano over two weeks.

• Step 1; Inputs: collect audio (interviews, FGDs), policy PDFs, and survey spreadsheets and label each file with commune and respondent metadata.
• Step 2; Ingest & clean: bulk upload; run auto-transcription with custom dictionary for local terms; enable PII redaction.
• Step 3; Code: import or build a codebook, use AI-assisted initial coding, then review and lock codes.
• Step 4; Analyze: run thematic analysis, frequency counts, and cross-segment comparisons (gender × commune × role).
• Step 5; Visualize & report: generate co-occurrence networks and hierarchical code trees; export a findings brief with quoted evidence and method notes.
• Step 6; Iterate: deploy AI-avatar follow-ups to gather data on training uptake or subsidy impact after intervention.

Ethics and reproducibility are central to replication: the PLOS study was approved by Nong Lam University ethics committee and restricted raw-data sharing for privacy reasons, so replicate responsibly, secure consent, anonymize transcripts, and document codebook decisions for transparency.

The conclusion is that combining reproducible qualitative coding with segment-level quantification strengthens the case for funding and program change when analysing gender dynamics in low-emission food systems or policy-to-practice gaps.

Try a 2-week Evidano pilot to import transcripts, run thematic and cross-segment analysis, and produce a stakeholder brief that pairs quotes with counts: Try Evidano for free. Read the original study at PLOS Sustainable Transformation.