Evidano is an AI-powered qualitative data analysis platform that automates transcription, PII redaction, AI-assisted coding, cross-segment analysis, and secure handling. Last week’s launch of New York State’s “Cannabis Honestly” campaign (reported July 20, 2026) followed a yearlong listening effort: 23 facilitated sessions with more than 450 young people, parents, educators, and community members. This post shows researchers and UX/policy teams how to reproduce a rigorous qualitative analysis of youth cannabis attitudes from that kind of dataset, faster and more defensibly, and how to operationalize findings with Evidano. Read the original write-up at Gotham Gal for context; below we map the listening to analysis to action workflow you can run this week.
Key Takeaways
The New York 'Cannabis Honestly' listening (23 sessions, more than 450 participants) revealed a clear knowledge gap among teens and parental concern about unlicensed supply, and those insights can be reproduced with a short, automated qualitative pilot. A 2-week workflow that combines automated transcription, PII redaction, AI-assisted coding, and cross-segment analysis will surface the same themes and produce stakeholder-ready outputs.
- New York data: 23 facilitated sessions, more than 450 participants, and a core signal that roughly one-third of teens could not name the legal purchase age.
- Practical pilot: a 2-week, 7-step workflow (transcribe, code, cross-segment, validate) reproduces listening insights and yields actionable recommendations.
- Design implications: clarify legal age messaging, address unlicensed supply and product safety concerns, and pair conversation guides with place-based enforcement metrics.
Fast take + source
Fast take: New York’s campaign grew from 23 facilitated listening sessions and more than 450 participants, aimed at giving families conversation tools while highlighting enforcement gaps.
- Source (first report): Gotham Gal (July 20, 2026).
- Core signal: ~1/3 of teens could not identify the legal age to consume cannabis, a clear knowledge gap.
- Design implication: conversation guides help, but enforcement and unlicensed supply remain structural issues.
Findings snapshot
| Date | Sessions | Participants | Key metric / finding | Source |
|---|---|---|---|---|
| July 2026 | 23 | More than 450 | Roughly 1/3 of teens could not name legal purchase age (NY) | Gotham Gal |
What the listening revealed (plain English)
The New York listening revealed three concrete pain points: knowledge gaps among youth about legal rules, parental anxiety about unlicensed supply and product safety, and mixed messaging across channels.
- The listening surfaced attitudinal themes (confusion, fear), behavioral signals (underage access via illicit channels), and contextual drivers (retail landscape).
- Sampling: 23 sessions and more than 450 participants are enough to surface consistent themes but not to quantify prevalence across the state.
- Interpretation caveat: facilitated sessions reveal depth and patterns, not population-level rates; follow-up surveys or administrative data are needed for prevalence.
So what for researchers, UX teams, and policy analysts
Overview
This section lists practical actions for researchers, UX teams, and policy analysts based on the listening.
Researchers
Researchers should turn session transcripts into reproducible codebooks and triangulate qualitative themes with short surveys to estimate prevalence.
Turn session transcripts into reproducible codebooks, capture theme frequency (confusion, safety concerns), co-occurrence (age confusion plus access), and sentiment about enforcement. Recommendation: triangulate with a short follow-up survey to estimate how widespread the identified confusion is.
UX & communications teams
UX and communications teams should prioritize clarifying legal age messaging and test message frames with parents and teens separately.
Design decision: prioritize clarifying legal age and safe-purchase signage, and test message frames with parents and teens separately. Use segmented messaging, for example teens respond to peer-sourced information while parents need concrete safety-check behaviors.
Policy & enforcement
Policy and enforcement teams should pair conversation guides with place-based enforcement metrics to close the gap between message and market reality.
Operational move: pair conversation guides with place-based enforcement metrics to close the gap between message and market reality. Measure success beyond reach by tracking changes in unlicensed sales reports and calls to regulators after campaign rollout.
Do more, faster with Evidano
Overview
Evidano helps teams automate transcription, PII redaction, AI-assisted coding, cross-segment analysis, and secure handling so teams can do more, faster.
Problem: 23 sessions mean dozens of hours of audio and messy transcripts. Evidano automates transcription with a custom dictionary (slang, product names) and PII redaction so teams can code safely and quickly.
From audio to themes
Evidano converts audio to searchable transcripts and extracts thematic signals efficiently.
Problem: 23 sessions mean dozens of hours of audio and messy transcripts. Evidano automates transcription with a custom dictionary (slang, product names) and PII redaction so you can code safely and quickly.
Reliable, reproducible coding
Evidano enables reproducible codebooks and AI-assisted coding to produce theme-level frequency tables and co-occurrence analyses.
Problem: inconsistent human coding across coders. Evidano lets teams import or build a codebook, run AI-assisted coding, and get theme-level frequency tables and co-occurrence networks for hypothesis testing and stakeholder reports.
Segmented comparisons & quick validation
Evidano provides cross-segment analysis and clickable exemplar quotes so teams can compare teens, parents, and educators quickly.
Problem: need to compare teens versus parents versus educators. Evidano supports cross-segment analysis and clickable exemplar quotes to show what each group actually said and where they converge or diverge.
Follow-ups & scale
Evidano supports autonomous short surveys or interviews so teams can run quick follow-ups and ingest results for thematic comparison with original sessions.
Problem: limited capacity to run quick follow-ups. Evidano can deploy AI-avatar interviewers to run autonomous short surveys or interviews, then ingest results for thematic comparison with the original sessions.
Security & compliance
Evidano encrypts data and does not use customer data to train third-party models, which supports secure handling of youth data and sensitive topics.
Evidano note: data is encrypted and never used to train third-party models, helpful when working with youth data and sensitive topics.
Checklist: 7-step workflow to reproduce the NY listening (2-week pilot)
This checklist provides a 7-step workflow to reproduce the New York listening in a two-week pilot.
- 1) Gather inputs: audio, facilitator notes, screener data (demographics).
- 2) Transcribe: use Evidano transcription with a custom dictionary for local slang and product names; redact PII.
- 3) Rapid codebook: start with 10 to 12 a priori codes (legal knowledge, access, safety, enforcement, messaging), then refine after the first four sessions.
- 4) Batch-code: run AI-assisted coding across all transcripts; review and adjudicate edge cases.
- 5) Run cross-segment analysis: compare teens versus parents versus educators; export theme frequency tables and exemplar quotes.
- 6) Validate: run a 200-response micro-survey on key knowledge gaps (for example, legal age) to estimate prevalence.
- 7) Deliver: produce one-pager for stakeholders with top three actionable recommendations, a co-occurrence map, and a verbatim quote bank.
FAQ: qualitative analysis of youth cannabis attitudes
How generalizable are 23 sessions with about 450 people?
They are excellent for depth and pattern detection but not for estimating population prevalence.
Facilitated sessions reveal consistent themes and rich context, however targeted surveys or administrative data are required to estimate how widespread those themes are across a state or population.
Can AI accurately code youth slang about products?
AI can accurately code youth slang when teams provide a custom dictionary and perform iterative human review.
Evidano supports custom dictionaries and rapid human-in-the-loop corrections to improve accuracy and handle local terminology.
Is this approach appropriate for sensitive topics involving minors?
Yes, but only with appropriate consent, parental permissions where required, and secure handling including redaction.
Design for consent, obtain parental permissions where required, treat findings as non-diagnostic research material, and use secure handling and redaction to protect participant privacy.
How quickly can teams reproduce the NY workflow?
A focused two-week pilot following the 7-step checklist can reproduce the listening and produce actionable outputs for stakeholders.
Follow the checklist: gather inputs, transcribe and redact, build a rapid codebook, run AI-assisted coding, compare segments, validate with a micro-survey, and deliver a stakeholder one-pager.
Wrapping up: next moves
The New York 'Cannabis Honestly' campaign showed clear knowledge gaps among youth and concerns about unlicensed supply, which invite targeted communications and enforcement-aligned interventions.
- Run a 2-week pilot using the 7-step checklist above and accelerate synthesis with Evidano.
- Want help reproducing the NY workflow on your data? Visit Evidano to start a secure pilot; Evidano can ingest transcripts, run thematic and cross-segment analyses, and produce stakeholder-ready visualizations in days.
- Try Evidano for free to begin a pilot and reproduce these listening insights on your data.