Qualitative analysis of HIV disclosure decision aids is a practical staple for researchers and program teams adapting interventions across contexts. A July 16, 2026 PLOS One study used Nominal Group Technique (NGT) with 22 stakeholders in Tbilisi to pick a disclosure decision aid for adaptation in Georgia (selected: the Family disclosure DA; providers had favored the digital READY tool). Read the original paper at PLOS One. In this post we extract the methods-to-workflow mapping you can reuse: what to code, how to compare stakeholder segments (providers vs PLWH), and how to operationalize adaptation-ready outputs using Evidano (www.evidano.com), from transcription and translation to thematic and cross-segment analysis and visual reports.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that helps teams convert NGT notes and ranking data into adaptation-ready recommendations quickly.
A July 16, 2026 PLOS One NGT study with n = 22 stakeholders in Tbilisi found people living with HIV prioritized a Family disclosure decision aid, while providers favored the digital READY tool.
- Study snapshot: published July 16, 2026 in PLOS One, n = 22 (12 providers; 10 PLWH), recruitment Feb 9–Mar 3, 2026.
- Main contrast: PLWH prioritized one-to-one session-based Family disclosure DA (trust, individualized support); providers prioritized digital formats (READY) for scalability.
- Top adaptation asks to code and operationalize: peer educators, legal/treatment content, confidentiality/medication management, and flexible session sequencing.
Fast take + source
This section summarizes the study and its immediate implications for adaptation workflows.
- What happened: On July 16, 2026 Zurashvili et al. published an NGT study that ran separate consensus sessions with 12 HIV providers and 10 people living with HIV, then held a joint meeting to prioritize existing disclosure decision aids for adaptation to Georgia.
- Source: PLOS One.
- Headline result: PLWH prioritized an individual session-based Family disclosure DA; providers prioritized digital formats (READY).
- Why it matters: Stakeholder segmentation changes format preference and highlights adaptation priorities (peer educators, legal content, confidentiality).
Findings snapshot (key numbers & dates)
| Date / Metric | Value | Source | Implication |
|---|---|---|---|
| Study published | July 16, 2026 | PLOS One | Recent, peer-reviewed evidence to inform adaptation |
| Participants (total) | n = 22 (12 providers; 10 PLWH) | Methods section | Small, targeted stakeholder sample for NGT |
| Recruitment window | Feb 9 – Mar 3, 2026 | Methods section | Rapid engagement; urban (Tbilisi) sample |
| Formats reviewed | 9 interventions (session/paper/digital) | Desk review | Cross-domain evidence (HIV, mental health, substance use) |
| Final selection | Family disclosure DA (PLWH priority) | Joint session | Patient-centered choice prioritized over provider scalability preference |
| Top adaptation asks | Peer educators; legal/treatment content; flexible sessions | Discussion notes | Concrete items to incorporate in adaptation |
What happened; NGT in plain English
This section explains the NGT workflow the study used and how notes and rankings were analyzed.
The study team ran two 60-minute NGT sessions (providers; PLWH) followed by a 40-minute joint meeting, reviewed one-page summaries of nine candidate interventions grouped by format (session-based, paper-based, digital), generated pros and cons, and independently ranked formats and specific interventions within preferred formats; notes were de-identified and analyzed with descriptive content analysis.
- Providers scored digital highest for accessibility and scalability; PLWH scored session-based highest for trust and individualized support.
- Within formats: providers preferred READY (digital); PLWH preferred the Family disclosure DA (individual sessions focused on family disclosure).
- Adaptation priorities included peer delivery, legal guidance, confidentiality/medication management, and options for phased individual to group support.
So what for qualitative researchers and program teams: what to code and compare
For qualitative researchers
Qualitative researchers should code for format-specific affordances and barriers and for relational constructs to make adaptation decisions evidence-based.
Code for format-specific affordances and barriers (digital literacy, privacy, readability) and for relational constructs (trust, peer credibility, emotional readiness).
Capture stakeholder-prioritized adaptation items as discrete codes (peer educator, legal info, medication confidentiality) so these items can be quantified and tracked across cohorts.
For implementers / UX teams
Implementers and UX teams should compare segment-level preferences and map adaptation asks to minimum viable product features to support pilot planning.
Compare segment-level preferences (providers vs PLWH) using cross-segment frequency tables and co-occurrence of themes (for example, 'scalability' co-occurs with 'digital' among providers).
Map adaptation asks to MVP features: make an initial one-to-one session flow plus optional group modules; add legal FAQ and downloadable medication-privacy tips.
Ethics & safeguards (brief)
Teams working with PLWH data must follow safeguards including informed consent and secure storage to protect participants.
Research note: this is implementation-focused, non-diagnostic work. When working with PLWH data, ensure informed consent, de-identification, and secure storage; avoid using sensitive text in public demos.
Do more, faster with Evidano (map the study workflow to features)
Ingest and clean
Evidano ingests and cleans notes and spreadsheets to prepare data for analysis.
Import interviews, NGT flip-chart notes, and ranking spreadsheets. Use Evidano transcription with custom dictionary and PII redaction for sensitive mentions such as medication and names.
Translate & normalize
Evidano translates and normalizes non-English content while preserving technical terms for reliable coding.
If notes include Georgian-language content, run Evidano translation with your custom dictionary to preserve legal and clinical terms for reliable coding.
Thematic + frequency analysis
Evidano automates thematic extraction to propose candidate codes and supports AI-assisted coding for consistency.
Run automated thematic extraction to get candidate codes (trust, scalability, peer support), then refine with codebook import and AI-assisted coding to ensure consistency across sessions.
Cross-segment comparisons
Evidano provides cross-segment analysis to make differences explicit between stakeholder groups.
Use Evidano cross-segment analysis to compare providers versus PLWH: frequency tables, co-occurrence networks, and hierarchical code to subcode visualizations make differences explicit for stakeholders.
Stakeholder-ready outputs
Evidano generates visuals and quote packs for workshops and materials.
Generate downloadable visuals (word clouds, co-occurrence graphs) and clickable quote lists organized by code and segment for adaptation workshops and grant materials.
Secure by design
Evidano keeps sensitive data encrypted and does not use it to train third-party models.
Data is encrypted and never used to train third-party models on Evidano, a critical point when handling stigmatized-health data.
Actionable 6-step workflow: reproduce this study’s analysis in Evidano
This section gives a reproducible six-step workflow to take raw notes and rankings to adaptation-ready recommendations using the study as an example.
- 1) Import and de-identify session notes and ranking spreadsheets, and set PII redaction rules.
- 2) Transcribe audio and translate Georgian text using custom dictionaries to preserve legal and clinical terms.
- 3) Auto-extract themes; review and import a codebook (for example, 'format preference', 'trust', 'peer involvement', 'legal info').
- 4) Run cross-segment frequency and co-occurrence analyses comparing providers versus PLWH to surface diverging priorities numerically.
- 5) Create visuals and a ranked recommendations list (for example, prioritize Family disclosure DA; add peer delivery and legal modules).
- 6) Export a stakeholder-ready report and share a clickable quotes pack for adaptation workshops and pilot planning.
FAQ: qualitative analysis of disclosure decision aids
What counts as a theme worth adapting?
A theme is worth adapting when it is frequent across PLWH or co-occurs with readiness markers that predict uptake.
Prioritize themes that are frequent across PLWH or that co-occur with readiness markers (for example, 'emotional readiness' plus 'preference for one-to-one').
How do I compare ranking scores and text notes together?
Triangulate ranking scores and coded text to explain why stakeholders preferred an option numerically and narratively.
Triangulate: treat ranking scores as quantitative preference indicators and use coded quotes to explain why segments ranked an option higher or lower.
How do we protect participants when sharing quotes?
Protect participants by redacting identifiers, paraphrasing, and using aggregated counts in public materials.
Always redact identifiers, paraphrase when necessary, and store raw files encrypted. For public materials, use aggregated counts and paraphrased examples.
Conclusion, next steps
This conclusion summarizes the recommended next steps for teams adapting disclosure decision aids and links to study and product resources.
If you are adapting a disclosure decision aid, start by segmenting stakeholders (providers versus PLWH), code for relational and practical adaptation needs, and translate those codes into MVP features (peer delivery, legal content, flexible session sequencing).
- Run a two-week pilot analysis in Evidano to turn raw notes and rankings into an actionable adaptation plan, visit Evidano to get started or request a demo tailored to sensitive-health workflows.
- Original study: PLOS One.
- Get started: Try Evidano for free.