Speed Up Qualitative Sustainability Assessment

Evidano is an AI-powered qualitative data analysis platform that speeds PSAT-style mixed-methods sustainability assessments from multilingual audio through coding to decision-ready briefs.

The Rwanda MDA PSAT (25 June 2026) produced a moderate overall sustainability score (3.75/5), with partnerships highest (4.1) and funding stability lowest (3.3); teams can convert those findings into prioritized action in 10–14 days using AI-assisted workflows.

• The Rwanda study: overall 3.75/5 (n=21 qualitative informants, 17 PSAT respondents), partnerships 4.1, funding stability 3.3, strategic planning 3.5.
• Automated transcription, translation, and AI-assisted coding reduce manual review hours and inter-coder drift when mapping open responses to the PSAT’s eight domains.
• Cross-segment frequency and co-occurrence analyses reveal where problems cluster (e.g., funding with data quality), enabling targeted interventions.
• Evidano supports PII redaction, end-to-end encryption, and a no-third-party-model-training policy to meet donor and ethical requirements.

The findings snapshot summarizes the key metrics and sources cited in the Rwanda MDA PSAT study.

See the table below for quick reference to sample sizes, domain means, and the original publication source.

The PSAT mixed-methods application combined an adapted Program Sustainability Assessment Tool and open-ended interviews to assess eight sustainability domains in Rwanda’s MDA program.

The team used an adapted Program Sustainability Assessment Tool (PSAT) and open-ended interviews to assess eight sustainability domains (political support, funding stability, partnerships, organizational capacity, program evaluation, program adaptation, communications, strategic planning). Surveys were completed electronically (Qualtrics) and interviews were audio-recorded in Kinyarwanda, transcribed verbatim, and translated into English.

• Design: Mixed-methods PSAT plus deductive thematic coding aligned to PSAT domains.
• Sample: 21 stakeholders (government, donors, implementers); 17 completed PSAT.
• Key numeric takeaways: overall 3.75/5; partnerships 4.1; funding stability 3.3; strategic planning 3.5.
• Operational notes relevant to qualitative workflows: multilingual audio, decentralized data sources, variable denominator estimation across districts.

Mixed-method PSAT outputs provide comparable domain scores and explanatory quotes that guide where to prioritize sustainability investments and interventions.

These results matter for qualitative teams because they highlight how mixed-method PSAT outputs produce both domain scores (comparable metrics) and rich explanatory quotes that reveal causes (e.g., donor dependence, understaffing, data fragmentation). Turning these into action requires fast, trustworthy qualitative synthesis and cross-segment comparison.

• Translate domain scores into decision levers: treat funding stability (3.3) as a priority risk register item; use partnerships (4.1) as leverage for domestic advocacy.
• Use interview excerpts to validate who experiences the problem (central vs district staff) and where interventions should focus (data systems, workforce, budgeting).
• Standardize denominator/target definitions across districts by extracting and comparing coded phrases that describe local methods (e.g., 'ubudehe lists', 'CHW assessments').

This two-week pilot checklist shows how to reproduce a PSAT mixed-methods synthesis in Evidano in 10–14 days.

Run-book to go from raw audio plus PSAT scores to a decision brief in 10–14 days:

• Day 0–2: Ingest files, upload audio, PSAT spreadsheets, and any district reports to Evidano; set project language (Kinyarwanda) and add custom dictionary terms (imihigo, ubudehe).
• Day 3–5: Auto-transcribe plus translate; run PII redaction; review and correct transcripts (accuracy boost where local terms appear).
• Day 5–7: Import PSAT domain codebook; run AI-assisted coding to tag excerpts to the eight PSAT domains; review and finalize codes.
• Day 8–10: Run cross-segment frequency and co-occurrence analyses (e.g., funding mentions by district vs central); generate top themes and illustrative quotes per domain.
• Day 11–12: Create visuals (domain score table, quote packs, co-occurrence network) and assemble a 1-page decision brief with recommended actions tied to domain scores.
• Day 13–14: Share interactive report link with stakeholders, collect comments, and produce a brief FAQ and next-step backlog for budget/HR interventions.

AI-assisted workflows can cut manual synthesis time and improve comparability for PSAT-style mixed-methods sustainability assessments.

If your team runs PSAT-style mixed-methods or any qualitative sustainability assessment, you can cut manual synthesis time and improve comparability with AI-assisted transcription, translation, codebook import, and cross-segment analysis. For the Rwanda MDA study specifically, prioritize funding-stability scenarios, workforce planning, and data-standardization workstreams, all of which you can accelerate as described above.

• Ready to pilot? Upload one campaign’s audio plus PSAT spreadsheet to Evidano and follow the two-week checklist.
• For security-sensitive projects, note Evidano’s encryption and data governance options before onboarding stakeholders.

To start a pilot or request a demo, Try Evidano for free.