Researchers and program teams need reproducible ways to compare stakeholder priorities for sensitive interventions. This post distills a PLOS One study (published 16 July 2026) that used the Nominal Group Technique (NGT) with n=22 stakeholders (providers n=12; people living with HIV, PLWH, n=10) to choose an HIV disclosure decision aid for adaptation in Georgia, and shows a reproducible workflow to recreate the study analysis using an AI qualitative analysis platform. You will get the study’s key numbers, what drove the provider/PLWH split, and a short two-week workflow to import session notes, run thematic and cross-segment analyses, and deliver visuals and a stakeholder brief in days instead of weeks.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that helps teams reproduce NGT-style analyses, generate cross-segment visuals, and produce exportable stakeholder briefs.
This post summarizes Zurashvili et al., PLOS One (16 July 2026), an NGT study in Tbilisi where a joint consensus of 22 stakeholders prioritized adapting the Family disclosure decision aid over digital options.
- Study snapshot: n=22 participants (providers n=12; PLWH n=10), recruitment Feb 9–Mar 3, 2026, final consensus published 16 July 2026.
- Stakeholder split: providers favored digital formats for feasibility and scale, PLWH favored session-based individual Family disclosure DA for trust, confidentiality, and peer support.
- Adaptation priorities: include peer educators, local legal content, medication confidentiality, treatment adherence, and flexible session timing and delivery.
- Reproducible workflow: collect one-page intervention summaries, de-identified ranking sheets, and audio/notes; transcribe and run cross-segment thematic analysis to generate visuals and a concise adaptation brief within two weeks.
Fast take: qualitative analysis of HIV disclosure decision aids
The fast answer is: Zurashvili et al. used three NGT sessions in Tbilisi and the joint consensus (published 16 July 2026) selected adapting the individual Family disclosure decision aid rather than digital options.
The study procedures and numbers show why the Family disclosure DA was prioritized and what adaptation content stakeholders requested, see the full study in PLOS One: PLOS One article.
- Sample: 22 participants (12 HIV care providers, 10 people living with HIV).
- Providers ranked digital formats highest; PLWH ranked session-based (individual) approaches highest.
- Consensus prioritized the Family disclosure DA with adaptation priorities: peer educators, local legal content, flexible session timing.
Findings snapshot
| Item | Value / detail | Source note |
|---|---|---|
| Publication date | 16 July 2026 | PLOS One |
| Participants | n=22 (providers 12; PLWH 10) | Recruitment Feb 9–Mar 3, 2026; Tbilisi |
| Provider top format | Digital (READY favored) | Accessible and scalable, providers emphasized feasibility |
| PLWH top format | Session-based (Family disclosure DA) | Trust, individualized support, peer role emphasized |
| Final consensus | Adapt Family disclosure DA for Georgia | PLWH preferences prioritized in joint session |
What happened, methods & mechanics
The study ran two separate NGT sessions (providers; PLWH) followed by a 40-minute joint session to prioritize interventions for adaptation.
Before the sessions the study team compiled one-page summaries for nine evidence-based interventions (three session-based, three paper-based, three digital). Participants silently generated ideas, discussed pros and cons, and ranked formats and specific interventions using a three-point allocation per person; notes were recorded as de-identified field notes and ranking sheets were aggregated.
- Interventions reviewed spanned HOP/POP, Family disclosure DA (seven individual sessions over ~12 months), CORAL booklets, READY (seven short digital modules), and 'Who, When, How to Share'.
- Providers emphasized feasibility, scalability, and digital access; PLWH emphasized trust, confidentiality, peer educators, and guidance for family disclosure.
- Adaptation requests included legal guidance, medication confidentiality, treatment adherence content, and flexibility in session timing and delivery.
Implications for researchers & UX teams
Why stakeholder splits matter
Stakeholder splits matter because providers prioritized digital formats for system-level feasibility while PLWH prioritized trust and individualized support after diagnosis.
Provider preferences for digital reflect system-level constraints such as scale and staffing, while PLWH preferences reflect immediate emotional and relational needs; when adapting decision aids prioritize end-user readiness and safety even if that increases delivery cost.
In this study PLWH preferences guided the final selection, and adaptation planning should balance both perspectives to support implementation feasibility.
Design priorities to capture
The key design priority is to start individual (one-to-one) sessions immediately after diagnosis, then offer group or peer supports optionally.
Include locally relevant legal and rights content and explicit guidance for non-HIV healthcare interactions (for example dentists or general practitioners).
Build flexible session length and optional digital adjuncts to improve scalability without removing person-centered support.
Do more, faster with Evidano
Import messy inputs, transcripts, notes, rankings
Evidano ingests session notes, de-identified ranking spreadsheets, and supporting PDFs so teams can recreate the NGT evidence base in one workspace.
If audio from sessions is available, use Evidano transcription with a small custom dictionary and PII redaction to generate verbatim transcripts for richer coding.
Run reproducible qualitative and cross-segment analysis
Evidano generates thematic and frequency analyses across stakeholder segments (providers versus PLWH) to quantify preference splits and surface adapted-content priorities.
Use cross-segment comparisons in Evidano to produce evidence tables (for example format preference by age and gender) like the study’s ranking summaries but reproducible and exportable.
Deliver visuals and stakeholder-ready outputs
Evidano auto-generates word clouds, co-occurrence networks, and hierarchical code-to-subcode visuals to show why PLWH prioritized individual sessions (trust, confidentiality, peers).
Evidano exports clickable quotes and a concise stakeholder brief for adaptation workshops and funding proposals.
Prototype adaptive delivery (optional)
Evidano AI avatar interviewers can pilot one-to-one decision-aid sessions or digital adjuncts, collecting standardized responses for iterative refinement.
All data is encrypted and never used to train third-party models, which is useful for sensitive health work.
Two-week pilot workflow to reproduce the study analysis
This two-week run-book reproduces an NGT-style prioritization and produces adaptation recommendations.
- Day 0–2: Gather materials, one-page intervention summaries, field notes, de-identified ranking spreadsheets, and any audio files.
- Day 2–4: Import documents and ranking CSV into Evidano. Run transcription on audio (if any) and apply a small custom dictionary for local terms and organizations.
- Day 4–6: Auto-generate themes and run cross-segment frequency counts (providers versus PLWH).
- Day 6–9: Produce visuals (co-occurrence network, hierarchical codes) and extract ten representative, de-identified quotes per theme.
- Day 9–11: Draft a concise adaptation brief and an implementation checklist based on top themes (peer role, legal content, flexible sessions).
- Day 11–14: Share an interactive report with stakeholders for a rapid joint session and collect ranking updates; export a final decision memo.
FAQ: qualitative analysis of HIV disclosure decision aids
Q: How do I compare segment rankings reliably?
A: Import ranking spreadsheets as structured data in Evidano and run cross-segment frequency and significance checks to compare stakeholder segments reliably.
Visualize differences with side-by-side tables and network graphs to make ranking splits interpretable for stakeholders.
Q: The study used field notes not verbatim transcripts, is that sufficient?
A: Field notes can support rapid consensus but verbatim transcripts improve coding depth and reproducibility.
If possible, transcribe audio and re-run thematic coding to validate initial findings and capture more granular quotes.
Q: What was the study sample and recruitment timeframe?
A: The study sample was n=22 participants, with providers n=12 and PLWH n=10, recruited Feb 9–Mar 3, 2026 in Tbilisi.
Use de-identified participant codes and clear recruitment dates when you replicate the study to preserve transparency.
Q: Which intervention formats did the study review?
A: The study reviewed nine interventions: three session-based, three paper-based, and three digital options.
Examples included Family disclosure DA (seven individual sessions), READY (seven short digital modules), CORAL booklets, HOP/POP, and 'Who, When, How to Share'.
Q: How do I reproduce the study analysis with Evidano?
A: Reproduce the study by importing one-page intervention summaries, de-identified ranking sheets, and field notes or transcripts into Evidano, then run thematic and cross-segment analyses to create visuals and an adaptation brief.
Follow the two-week pilot workflow above and share outputs with stakeholders for a rapid joint consensus session.
Conclusion, next steps & CTA
Zurashvili et al. (16 July 2026) provide a compact example of how structured consensus (NGT) surfaces differing priorities between providers and PLWH and how those differences should shape adaptation choices.
Researchers and program teams should use a reproducible pipeline: verbatim transcripts or high-quality notes, clear ranking inputs, cross-segment comparisons, and stakeholder-ready visuals to support adaptation and implementation planning (see the original study in PLOS One: PLOS One article).
Ready to reproduce this analysis on your own NGT sessions or transcripts? See how Evidano speeds import to thematic and cross-segment analysis and produces exportable briefs in days: Try Evidano for free.
Ethics note: this guidance is research-focused and non-diagnostic; follow local consent and confidentiality requirements when handling sensitive health data.