Youth-engaged qualitative analysis addresses how to partner young people as collaborators on coding, theme development, and manuscript writing, and is essential for studies of marginalized youth. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. This post explains the primary lessons from the RISES study for research teams and describes practical, AI-enabled workflows teams can use to train youth advisory boards and scale participation.
Key Takeaways
According to the PLOS One article by Pham et al. (2026), a five-member transgender and gender-diverse youth advisory board was trained and retained to participate in qualitative coding and analysis across a one-year RISES study.
According to the PLOS One article, the study reached an 83.3% YAB retention rate after one year and had 100% of the trained YAB members complete qualitative analysis training.
- 08/04/2024 recruitment start: 12 screening surveys completed and 8 interviews conducted, according to PLOS One (Pham et al., 2026).
- In 2025 the RISES study coded 42 transcripts, with YAB members and mentors coding 25 transcripts, according to PLOS One (Pham et al., 2026).
- Each YAB member coded 2–6 transcripts, according to PLOS One (Pham et al., 2026).
- YAB compensation was adjusted to $125 every 3 months based on participant feedback, according to PLOS One (Pham et al., 2026).
What Happened: RISES youth-engaged qualitative analysis
The RISES project partnered with a five-member transgender, non-binary, and gender-diverse youth advisory board (YAB) to conduct qualitative analysis, according to the PLOS One report (Pham et al., 2026).
According to PLOS One (Pham et al., 2026), recruitment began on 08/04/2024, 12 people completed the screening survey, 8 completed the interview and follow-up task, 6 were invited to consent, and 5 youth participated long term.
According to PLOS One (Pham et al., 2026), the team provided formal didactic training, NIH Good Clinical Practice certification, hands-on coding practice using Braun and Clarke’s six-step reflexive thematic analysis, and one-on-one mentorship from graduate and medical students.
According to PLOS One (Pham et al., 2026), YAB members coded 2–6 transcripts each and the YAB/mentor pairs coded 25 of 42 transcripts, with all transcripts double coded by TGD-identifying team members where possible.
According to PLOS One (Pham et al., 2026), communication practices shifted to encrypted group messaging (Signal), flexible meeting schedules, and mentor-led check-ins to reduce burden on youth.
According to PLOS One (Pham et al., 2026), YAB members wrote feedback on themes and supporting quotes and one YAB member met ICMJE authorship criteria and coauthored the paper.
According to PLOS One (Pham et al., 2026), the team reported YAB comments such as "Thank you so much, I am so grateful to have you guys as support! !" which the authors used to document the supportive group environment.
Findings Snapshot
| Date | Metric | Value | Implication (as reported in PLOS One) |
|---|---|---|---|
| 08/04/2024 | Recruitment start | 12 screening surveys completed, 8 interviews completed, 6 invited | PLOS One (2026): multi-step screening and reflective tasks supported selection of 5 long-term YAB members |
| 2025 | Transcripts coded | 42 total transcripts; 25 coded by YAB members/mentors | PLOS One (2026): YAB and TGD research staff double-coded many transcripts to balance rigor and timeline |
| After 1 year (2025–2026) | YAB retention | 83.3% retention | PLOS One (2026): regular compensation and feedback cycles correlated with sustained engagement |
| During coding period (2025) | Training completion | 100% of YAB members trained in qualitative analysis | PLOS One (2026): combined didactic sessions, hands-on practice, and mentorship enabled full training |
| Ongoing | Per-member coding load | 2–6 transcripts per YAB member | PLOS One (2026): flexible assignments allowed youth to balance coding with school and work |
Implications for qualitative researchers and youth-engaged teams
Youth-engaged qualitative analysis requires planning for training, mentorship, compensation, and flexible timelines, according to PLOS One (Pham et al., 2026).
According to PLOS One (Pham et al., 2026), compensating youth more frequently and building mentor relationships increased retention and quality of coding, which implies project budgets must allocate recurring micro-payments and mentor time.
- Allocate funds for ongoing compensation: PLOS One (2026) moved from $250/6 months to $125/3 months based on youth feedback.
- Design training that combines short didactic sessions with hands-on practice: PLOS One (2026) used three 45-minute didactics plus practice transcripts tied to youth interests.
- Embed mentorship: PLOS One (2026) paired each youth with a trained mentor who checked in weekly to sustain progress and reduce overwhelm.
How Evidano Helps
Problem: Slow, manual synthesis of youth-coded transcripts
Solution: Evidano speeds synthesis by auto-ingesting transcripts and producing thematic, content, and frequency analyses that researchers can review, which reduces time-to-insight compared to manual aggregation.
Evidano’s AI chat over your documents lets mentors and youth ask targeted questions of the coded data and retrieve exemplar quotes and code frequencies instantly, improving feedback loops.
Problem: Training, documentation, and PII handling for sensitive youth data
Solution: Evidano supports secure transcription with custom dictionaries and PII redaction to keep sensitive youth data protected during training and coding.
Evidano’s speech-to-text and data-security features let teams centralize transcripts, ensure compliance, and provide consistent materials for YAB training.
Problem: Mentor burden and asynchronous coordination
Solution: Evidano’s cross-segment analyses and visualizations let mentors and youth compare code co-occurrence and segment-level patterns without re-coding in spreadsheets, which aligns with the mentorship-driven approach used in RISES.
For teams that want a single place to store transcripts, codebooks, and iterative code revisions, Evidano’s features centralize collaboration while preserving audit trails.
FAQ: youth-engaged qualitative analysis
What is youth-engaged qualitative analysis and why use it?
Answer: Youth-engaged qualitative analysis means training young people to collaborate as coders and analysts on qualitative data.
According to PLOS One (Pham et al., 2026), this method increases ecological validity, empowers minoritized youth, and produces insights that researchers alone may miss.
How did the RISES project train youth to code transcripts?
Answer: The RISES project combined short didactic sessions, hands-on practice with themed transcripts, and one-on-one mentorship.
According to PLOS One (Pham et al., 2026), RISES used three 45-minute virtual trainings, practice transcripts tied to youth interests, NIH Good Clinical Practice certification, and weekly mentor check-ins.
What measurable outcomes did youth engagement produce in RISES?
Answer: The RISES study achieved high training completion and sustained participation with measurable coding outputs.
According to PLOS One (Pham et al., 2026), 100% of trained YAB members completed qualitative analysis training, the YAB coded 25 of 42 transcripts with mentors, and the project retained 83.3% of YAB members after one year.
Can AI tools help scale youth-engaged qualitative methods?
Answer: Yes, AI tools can accelerate synthesis, maintain secure document workflows, and reduce repetitive tasks so mentors and youth focus on interpretation.
Evidano provides thematic and cross-segment analysis, secure transcription, and an AI chat over documents so teams can preserve participatory practices while accelerating reporting and feedback cycles.
Conclusion & Next Steps
The RISES PLOS One study (Pham et al., 2026) demonstrates that with training, mentorship, flexible timelines, and compensation, youth advisory boards can successfully conduct qualitative analysis.
According to PLOS One (Pham et al., 2026), investing in communication and mentorship drove an 83.3% retention rate and full completion of training by all YAB coders.
Teams that want to replicate RISES-style youth-engaged qualitative analysis can combine the study’s methods with AI-enabled workflows to lower synthesis time and protect sensitive data.
If you want to pilot a workflow that pairs trained youth collaborators with AI-assisted synthesis, Try Evidano for free to ingest transcripts, run thematic analyses, and support mentor-led, participatory coding.