This post explains how to run youth-engaged qualitative analysis and why research teams should adopt it, using the July 30, 2026 PLOS ONE study as a worked example. The primary keyword is youth-engaged qualitative analysis and the audience is qualitative researchers and UX/health teams who want step-by-step, evidence-backed ways to train, retain, and ethically involve youth as coders. According to the PLOS ONE article, the Research on Identity Specific Eating Disorder Symptoms (RISES) team partnered with a five-member transgender and gender-diverse youth advisory board to co-develop a codebook and code transcripts, reporting concrete retention and productivity metrics that you can replicate.
Key Takeaways
According to the PLOS ONE article published July 30, 2026, a five-member transgender and gender-diverse youth advisory board (YAB) was trained and contributed to qualitative coding, with measurable retention and workload outcomes.
- The PLOS ONE study reports a YAB retention rate of 83.3% after one year (reported July 30, 2026).
- The PLOS ONE study reports that 100% of YAB members trained in qualitative analysis completed their training (reported July 30, 2026).
- The PLOS ONE study reports that YAB members coded between 2 and 6 transcripts each and that the YAB/mentors coded 25 of 42 transcripts during the project (data from 2025–2026).
- Recruitment for the RISES YAB began on 08/04/2024 and the qualitative interviews that informed the YAB work involved 21 transgender and gender-diverse youth aged 12–22 in 2025, according to PLOS ONE.
What happened and how the youth-engaged process worked
Answer: The PLOS ONE study embedded five transgender and gender-diverse youth as advisors and coders across a qualitative project to test youth-engaged qualitative analysis in practice.
According to the PLOS ONE article, recruitment for the youth advisory board began on 08/04/2024 and used screening (REDCap) plus interviews and a follow-up task to invite candidates into the board.
According to the PLOS ONE article, the RISES team provided formal training including NIH Good Clinical Practice coursework and three 45-minute didactic sessions, then paired each youth with at least one trained mentor for hands-on coding practice.
According to the PLOS ONE article, the team applied Braun and Clarke’s six-step reflexive thematic analysis, had each transcript coded by two team members, and adjusted plans so that YAB members/mentors ultimately coded 25 of the 42 transcripts to meet timeline and workload constraints.
According to the PLOS ONE article, the team tracked engagement using meeting attendance, contributions to a codebook, number of transcripts coded, and the brief Research Engagement Survey Tool (REST).
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 08/04/2024 | YAB recruitment opened | Screened 12, interviewed 8, invited 6, final YAB 5 | Use staged screening + practical task to assess readiness, per PLOS ONE |
| 2025 | Qualitative interview sample | 21 TGD youth aged 12–22 interviewed | Prioritize representation when designing youth-informed codebooks |
| Coding period 2025–2026 | Transcripts coded by YAB/mentors | 25 of 42 transcripts coded by YAB/mentors; each YAB member coded 2–6 transcripts | Balance coding load with mentor support to meet timelines |
| After 1 year (reported 07/30/2026) | YAB retention | 83.3% retention of YAB members | Compensation and flexible scheduling improved retention, per PLOS ONE |
| After training (reported 07/30/2026) | Training completion | 100% of recruited YAB members trained in qualitative analysis | Structured didactic + hands-on practice yields full training uptake |
Implications for qualitative researchers and research teams
Answer: Researchers should treat youth engagement as a design choice that affects recruitment, training, timeline, compensation, and analysis fidelity.
According to the PLOS ONE article, clear expectations, mentorship, flexibility, and frequent, modest compensation (changed from $250/6 months to $125/3 months) increased participation and retention.
According to the PLOS ONE article, mentorship pairs, asynchronous options, and encrypted group messaging (Signal) reduced barriers to participation and improved attendance when the research coordinator sent reminders.
According to the PLOS ONE article, teams should plan that youth coding can be productive but is time intensive: the RISES team originally planned full double-coding by YAB members but adapted to keep work double-coded by a combination of YAB members and experienced TGD research staff to complete coding within three months.
How Evidano helps with youth-engaged qualitative analysis
What is Evidano?
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, structured training plus mentorship was crucial for quality coding; Evidano accelerates those workflows by providing secure transcription, thematic coding, and AI-assisted synthesis to reduce manual burden.
Problem: Training and hands-on practice is time consuming
Solution: Use Evidano to generate clean transcripts and searchable source documents so mentors and youth can focus on coding practice rather than file prep.
Evidano supports automated speech-to-text with custom dictionaries and PII redaction, matching the PLOS ONE recommendation to reduce technical barriers for youth coders.
Problem: Mentorship and iterative codebook development is hard to track
Solution: Evidano provides collaborative codebooks, versioning, and AI-assisted thematic suggestions so mentors can review youth codes, reconcile discrepancies, and document decisions.
Evidano’s AI chat and document search features mirror the PLOS ONE emphasis on mentoring and documentation and let mentors provide timely feedback without duplicative file management.
Problem: Protecting privacy and maintaining trust
Solution: Evidano encrypts data and details data security practices so teams can follow the PLOS ONE team’s use of Signal and secure procedures when working with minoritized youth.
Evidano’s secure collaboration matches the PLOS ONE study’s use of encrypted group messaging and IRB-approved processes to protect youth contributors.
Problem: Summarizing themes for feedback and dissemination
Solution: Evidano produces extractable theme summaries, exemplar quotes, and visualizations so researchers can present preliminary themes back to youth advisory boards as the PLOS ONE study did when soliciting feedback.
For teams that want interactive review, Evidano’s AI chatbot can answer team questions about codes and pull supporting quotes for YAB review.
Contextual links
Learn more about platform features on the Evidano features page.
If you need secure transcription for mentor-led coding, see Evidano speech-to-text.
FAQ: youth-engaged qualitative analysis
How do you train youth to code transcripts reliably?
Answer: Provide formal didactic sessions plus hands-on practice and one-on-one mentorship, then validate with double-coding and reconciliation.
According to the PLOS ONE article, the RISES team used NIH Good Clinical Practice coursework, three 45-minute didactics, paired mentorship, and practice transcripts before live coding, which resulted in 100% of trained YAB members completing training (reported July 30, 2026).
What compensation and scheduling practices improve retention?
Answer: Offer frequent, modest payments and flexible asynchronous options aligned with youth schedules.
According to the PLOS ONE article, changing compensation from $250 every six months to $125 every three months and offering asynchronous tasks improved retention to 83.3% after one year (reported July 30, 2026).
Can youth co-author qualitative papers and still meet ethical standards?
Answer: Yes, when youth meet authorship criteria and are supported with mentorship and ethics training.
According to the PLOS ONE article, one YAB member met ICMJE authorship criteria with mentor support and the team required NIH Good Clinical Practice training prior to engagement (reported July 30, 2026).
How can AI tools support youth-engaged qualitative analysis without replacing youth expertise?
Answer: Use AI to automate preparation tasks and synthesize outputs while preserving youth judgment for coding decisions and theme confirmation.
According to the PLOS ONE article, youth contributed to codebook development and theme confirmation; Evidano’s AI can pre-process transcripts and surface patterns for YAB review, matching the study’s emphasis on youth-led interpretation.
Conclusion & Next Steps
Answer: The PLOS ONE RISES study demonstrates that youth-engaged qualitative analysis is feasible with deliberate training, mentorship, flexible logistics, and compensation (PLOS ONE, published July 30, 2026).
According to the PLOS ONE article, structured didactic sessions, one-on-one mentorship, and iterative feedback produced 100% training completion and an 83.3% retention rate after one year, while YAB members contributed substantive coding work and manuscript input.
If your team wants to scale youth-engaged analysis while preserving participant safety and code quality, consider combining the study’s human-centered practices with AI tools for transcription, secure collaboration, and synthesis.
Start a trial to pilot youth-engaged workflows and AI-assisted synthesis today: Try Evidano for free.