Evidano is an AI-powered qualitative data analysis platform that helps teams ingest, code, and visualize qualitative and mixed-methods program data. This post shows how to translate Rwanda’s 25 June 2026 PSAT mixed-methods findings into actionable, reproducible outputs using a 7-step workflow that mirrors the published study (n=21 interviews, 17 PSAT surveys). Read the original study at PLOS Neglected Tropical Diseases. Note: the original study had ethics approval (RNEC 471/2024); this blog focuses on methods and program decision-making, not clinical guidance.
Key Takeaways
Rwanda’s mixed-methods PSAT assessment scored moderate sustainability overall (3.75/5), with strong partnerships (4.1) and weak funding stability (3.3).
- Rwanda’s PSAT study published 25 June 2026 reported an overall score of 3.75 out of 5, based on 17 PSAT survey responses and 21 qualitative interviews.
- Top domain signal: partnerships scored 4.1; lowest domain signal: funding stability scored 3.3, reflecting heavy donor reliance (approximately 97% external financing in 2022/23).
- Actionable playbook: use the same mixed-methods logic and the 7-step workflow to create joint displays and stakeholder-ready briefs for funders and ministries.
- Operational monitoring: set up recurring ingests to track domain trends, especially funding stability and staffing, with 30/60/90 day action cycles.
Fast take + source
Rwanda’s MDA program scored 3.75 out of 5 on the Program Sustainability Assessment Tool (PSAT) in a mixed-methods study published 25 June 2026. Strengths included partnerships (4.1) and political support (3.9), while weaknesses included funding stability (3.3), staffing, and decentralized data use.
- Source: Mazimpaka et al., PLOS Neglected Tropical Diseases (published 25 June 2026).
- Study inputs: 21 qualitative interviews and 17 PSAT survey responses; the authors used mixed deductive coding aligned to eight PSAT domains.
- Payoff: replicate the study's mixed-methods logic to produce reproducible evidence and dashboards for funders and ministry decision-makers.
Findings snapshot
| Metric | Value | Source / Note |
|---|---|---|
| Published | 25 June 2026 | PLOS Neglected Tropical Diseases |
| Qual sample (interviews) | 21 stakeholders | Government, donors, implementers |
| PSAT respondents | 17 | Modified PSAT (LMIC-adapted) |
| Overall PSAT score | 3.75 / 5 | Moderate sustainability |
| Top domain | Partnerships, 4.1 | Strong multi-sector collaboration |
| Lowest domain | Funding stability, 3.3 | Heavy donor reliance (≈97% external financing in 2022/23) |
What the study did (plain English)
The study applied a mixed-methods PSAT assessment using a standardized eight-domain survey modified for low and middle income countries plus open-ended interviews mapped to the same domains.
- Surveys provided domain mean scores and standard deviations; interviews were transcribed, translated, and coded deductively against the PSAT domains.
- Integration approach: the authors used joint displays to compare domain scores with qualitative evidence and explained discrepancies, for example high political support with persistent data quality issues.
- Operational signals reported included expanded treatment to adults, HMIS integrations, decentralized tablet access, weak long-term financing, and staff shortages.
Implications for program teams, qualitative analysis of MDA sustainability
For policy & finance leads
Policy and finance leads should use PSAT domain-level scores to target budget advocacy for domestic line items and flexible operational funds.
Translate qualitative quotes into one-page evidence briefs for treasury and donors that highlight risk scenarios if donor flows change and emphasize the 3.3 funding stability score.
For program managers / implementers
Program managers should prioritize operational fixes where qualitative data points to process gaps such as denominator estimation and decentralized data access.
Design rapid pilots, for example district-level data ownership pilots, and track before and after themes and uptake using coded interview sets to measure change.
For researchers & evaluators
Researchers and evaluators should replicate the PSAT mixed-methods logic by aligning survey items with interview prompts, pre-registering analytic steps, and using joint displays to link scores to narrative evidence.
Validate translations and transcription accuracy as part of quality assurance, noting the study transcribed interviews in Kinyarwanda and translated them to English.
Do more, faster with Evidano (mapped to this use case)
Ingest & standardize documents
Ingest and standardize documents by dropping interview audio, transcripts, PSAT spreadsheets, and policy documents into Evidano.
Evidano supports auto-transcription and custom dictionaries for local terms, for example imihigo and ubudehe, to preserve meaning and cut preparation time from days to hours.
Reproduce the mixed-methods PSAT flow
Reproduce the mixed-methods PSAT flow by importing the PSAT survey spreadsheet and mapping questions to PSAT domains in Evidano.
Evidano runs frequency and cross-segment analyses, for example by stakeholder type such as government versus donor, and links qualitative codes to quantitative domain scores with joint-display exports.
Thematic, frequency & cross-segment analyses
Generate thematic, frequency, and cross-segment analyses by applying an AI-assisted PSAT codebook across transcripts and refining codes with manual review in Evidano.
Evidano produces theme frequencies, co-occurrence networks, and hierarchical code to subcode maps to rapidly identify recurring risks such as funding instability co-occurring with staff turnover.
Secure sharing & stakeholder-ready visuals
Create stakeholder-ready visuals and secure exports by using Evidano to generate word clouds, co-occurrence networks, and joint displays for ministry briefs.
Evidano encrypts data in transit and at rest, does not use customer data to train third-party models, and supports clickable quotes and contextualized excerpts for decision briefs and donor advocacy.
Continuous monitoring
Set up continuous monitoring by scheduling periodic ingests after MDA campaigns and automated comparison reports to track domain score trends over time.
Evidano's AI chat feature lets non-analysts query the corpus directly, for example: 'Show quotes where districts report denominator issues.'
Checklist: 7-step workflow to replicate a PSAT mixed-methods analysis
Follow this seven-step checklist to mirror the Rwanda study and produce stakeholder-ready outputs in Evidano.
- 1) Gather inputs: interview audio, transcripts in Kinyarwanda, PSAT survey spreadsheet, policy documents, and HMIS outputs.
- 2) Transcribe and translate in-platform using a custom dictionary for local terms and enable PII redaction where needed.
- 3) Import the PSAT spreadsheet and map survey items to the eight domains for automated scoring.
- 4) Auto-code transcripts with the PSAT-aligned codebook and review codes manually for reliability.
- 5) Run thematic, frequency, and cross-segment analyses and generate co-occurrence and hierarchical visualizations.
- 6) Build a joint display linking domain scores to illustrative quotes and export to a one-page policy brief.
- 7) Schedule recurring ingests to monitor trends and set alerts for domain declines, for example funding stability.
FAQ: qualitative PSAT analysis
How do I ensure translated quotes retain nuance?
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Use custom dictionaries and bilingual review to ensure translated quotes retain nuance.
Evidano supports translation with dictionary overrides and bilingual reviewer workflows to preserve terms like imihigo and ubudehe when extracting quotes for reports.
Can I compare PSAT domains by stakeholder type?
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Yes, you can compare PSAT domains by stakeholder type by importing stakeholder metadata and running cross-segment analyses.
Import metadata fields such as role, district, and organization to compare PSAT scores and theme prevalence across groups including government, donors, and implementers.
Is this secure for donor-funded or sensitive program data?
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Yes, this approach can be secure for donor-funded or sensitive program data when using encrypted platforms and PII redaction.
Evidano encrypts data at rest and in transit, does not use customer data to train third-party models, and provides PII redaction at ingestion.
Wrapping up: next steps & CTA
The Rwanda PSAT study published 25 June 2026 provides a domain-level roadmap: strengthen partnerships and political alignment while converting donor-driven gains into domestic flexible financing and stronger data ownership.
- Your next two moves: (1) replicate the mixed-methods joint display for your program using the 7-step workflow above; (2) build a 90-day monitoring feed that tracks funding stability and staffing signals across districts.
- See how fast this runs in Evidano: upload a transcript and PSAT sheet to get a demo analysis in under 48 hours. Try Evidano for free.
Topics
- PSAT mixed-methods
- MDA sustainability
- qualitative analysis
- Evidano
- Rwanda PSAT
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