Youth-engaged qualitative analysis answers how to include young people as collaborators in coding and theme development. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One article published July 30, 2026, youth-engaged research (YER) methods can train transgender and gender-diverse (TGD) youth to perform rigorous qualitative coding when teams provide training, mentorship, compensation, and flexible timelines (PLOS One). This post explains the PLOS One methods and lessons through an AI-enabled qualitative research lens and shows where AI tools can reduce workload while preserving engagement and ethical safeguards.
Key Takeaways
According to the PLOS One article (Pham et al., 2026), the RISES team partnered with a five-member transgender and gender-diverse youth advisory board (YAB) and successfully trained youth to conduct hands-on qualitative analysis across a year (PLOS One).
- The RISES project recruited 12 people who completed screening beginning August 4, 2024, invited 6 to join the YAB, and worked closely with 5 YAB members throughout 2025, according to PLOS One (Pham et al., 2026).
- According to PLOS One (Pham et al., 2026), the YAB retention rate was 83.3% after one year and 100% of trained YAB members completed qualitative analysis training in 2025.
- According to PLOS One (Pham et al., 2026), YAB members and mentors coded 25 of 42 transcripts (with 21 transcripts double coded for a total of 42 coded files), and each YAB member coded between 2 and 6 transcripts during the project.
What happened: how the RISES YAB ran youth-engaged qualitative analysis
Answer: The RISES team embedded five TGD youth as a youth advisory board and trained them to co-develop a codebook, code transcripts, review themes, and choose exemplar quotes, according to PLOS One (Pham et al., 2026).
According to PLOS One (Pham et al., 2026), recruitment began on August 4, 2024, with 12 completed screening surveys, 8 completed interview and follow-up tasks, 6 invited to consent, and a final working YAB of 5 members aged 16–21 in 2025.
According to PLOS One (Pham et al., 2026), the team used Braun and Clarke’s 6-step reflexive thematic analysis and structured the work with didactic training, hands-on coding practice, one-to-one mentoring, and iterative codebook development.
According to PLOS One (Pham et al., 2026), the YAB coded 25 of 42 transcripts (21 transcripts were double coded to create a total of 42 coded versions), and the principal investigator resolved coding discrepancies through discussion with YAB mentors and the research coordinator.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 08/04/2024 | Screening starts | 12 completed screening surveys | Narrow pool yielded 6 invited; recruitment task reduced attrition (PLOS One, 2026) |
| 2025 (coding period) | YAB size and composition | 5 TGD youth (ages 16–21), mixed gender identities and locations | Small, diverse YAB enabled meaningful collaboration but required flexible scheduling (PLOS One, 2026) |
| 2025 (analysis) | Transcripts coded | 25 of 42 transcripts coded by YAB/mentors; 21 transcripts double coded (42 coded files) | Double-coding improved rigor while redistributing workload to experienced TGD researchers (PLOS One, 2026) |
| After 1 year | Retention and training | 83.3% retention; 100% of trained YAB members completed qualitative analysis training | Training plus compensation and mentorship sustained engagement (PLOS One, 2026) |
| Throughout project | Compensation | $125 per 3 months plus $10 per transcript coded (payment schedule revised from $250/6 months) | More frequent pay improved retention and fairness (PLOS One, 2026) |
Implications for qualitative researchers and UX teams
Answer: Researchers should plan for resources, flexible timelines, and mentorship when involving youth in qualitative analysis, according to PLOS One (Pham et al., 2026).
According to PLOS One (Pham et al., 2026), compensating youth regularly (the RISES team changed compensation from $250 per 6 months to $125 per 3 months) and pairing each youth with a trained mentor helped the YAB maintain engagement and acquire coding skills.
According to PLOS One (Pham et al., 2026), asynchronous options reduced burden for non-coding tasks but YAB members preferred synchronous meetings for training and collaborative work, which suggests UX and research teams should budget for meeting facilitation.
According to PLOS One (Pham et al., 2026), teams working with minoritized youth should explicitly address power imbalances, offer choices of mentors, and check in on wellbeing when outside events affect communities.
How Evidano helps (problem → feature)
Problem: Training and onboarding youth coders takes time and repeated explanation
Solution: Evidano accelerates onboarding by providing structured, shareable training modules and example-coded transcripts so new YAB members can practice coding on familiar templates.
According to the PLOS One team (Pham et al., 2026), iterative hands-on practice and mentor pairing were central to capacity building; Evidano’s training workflows mirror that approach and let mentors review trainee code side-by-side in the platform (Evidano features).
Problem: Double-coding and discrepancy resolution is labor intensive
Solution: Evidano automates code co-occurrence reports, highlights coder disagreements, and generates summary visualizations so mentors and PIs focus on interpretation rather than file handling.
According to PLOS One (Pham et al., 2026), the RISES team double-coded 21 transcripts to ensure rigor; Evidano preserves those human judgments and speeds synthesis without replacing youth input (Evidano features).
Problem: Maintaining engagement and clear communication across schedules
Solution: Evidano supports asynchronous review, threaded comments on excerpts, and exports of concise summaries for group chat reminders, which matches the RISES finding that YAB members preferred concise updates and synchronous practice sessions (PLOS One, Pham et al., 2026).
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 co-create codebooks, code transcripts, and review themes as research collaborators rather than only participants, according to PLOS One (Pham et al., 2026).
According to PLOS One (Pham et al., 2026), YER centers lived experience, improves ecological validity, and is particularly important for understudied or mistrustful populations such as transgender and gender-diverse youth.
How did the RISES project train youth to code reliably?
Answer: The RISES project used didactic sessions, hands-on practice with example transcripts, one-to-one mentorship, and reflexive thematic analysis steps, according to PLOS One (Pham et al., 2026).
According to PLOS One (Pham et al., 2026), every YAB member completed NIH Good Clinical Practice training, attended three 45-minute didactic sessions, practiced coding with themed transcripts, and paired with mentors for weekly check-ins.
How can AI be used without undermining youth ownership of analysis?
Answer: Use AI to automate low-value tasks (transcription, initial code-frequency tables, and disagreement flags) while keeping coding and theme interpretation as youth-led activities, consistent with the ethical emphasis in PLOS One (Pham et al., 2026).
According to PLOS One (Pham et al., 2026), the project protected youth voice through mentorship and PI oversight; AI should support those human-led steps rather than replace them.
How should teams measure engagement and success for a youth advisory board?
Answer: Measure attendance, completion of training, number of transcripts coded, retention at defined timepoints, and qualitative feedback, which the RISES team did using attendance logs, the Research Engagement Survey Tool, and mentor evaluations (PLOS One, Pham et al., 2026).
According to PLOS One (Pham et al., 2026), the RISES team used REST, meeting attendance, and coding counts to quantify engagement while conducting one-on-one feedback sessions for professional development.
Conclusion & Next Steps
Answer: The PLOS One (Pham et al., 2026) RISES study demonstrates that youth-engaged qualitative analysis is feasible with dedicated training, mentorship, compensation, and flexible processes.
According to PLOS One (Pham et al., 2026), key supports were structured training, weekly mentor check-ins, privacy-minded communication tools, and revised compensation schedules that improved retention to 83.3% after one year.
If your team plans to involve youth in coding, map responsibilities, budget for mentor time and payments, and use AI tools to remove administrative friction while preserving youth leadership.
To trial AI-assisted, human-led qualitative workflows that respect participant privacy and youth ownership, Try Evidano for free.