A Youth-Centered Digital Infographic on Vaping Risks (What’s in a Vape?): Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND As youth engagement with traditional public health warnings declines, innovative strategies are needed. Visually compelling, youth-driven digital content such as interactive infographics may help bridge knowledge gaps, enhance risk perception, and support more informed decision-making. Despite this potential, limited research has assessed its effectiveness in conveying vaping-related harms to youth. OBJECTIVE To address this gap, this study evaluated the impact of a codeveloped, youth-informed digital infographic (What’s in a Vape?) on enhancing vaping education and improving youth understanding of vaping-related harms. METHODS A convergent parallel mixed methods design was used to assess the impact of a youth-informed digital infographic. The infographic was created in collaboration with youth coresearchers and youth advisory councils to ensure relevance. Participants were recruited through community partners, school boards, and youth networks. By May 2024, we had enrolled 63 high school students aged 14 to 19 years (mean age 16.5, SD 1.2 years) primarily from Ontario and British Columbia. The survey evaluated baseline knowledge of vaping, engagement with the infographic, and postexposure perceptions on whether the content contributed to increased awareness or understanding of vaping. RESULTS Data collection took place between April 2024 and May 2024. Quantitative analysis showed that 87% (55/63) of participants agreed that the infographic effectively communicated key information, and 86% (54/63) gained new knowledge about vaping. In addition, 73% (46/63) found that the infographic was presented in an easy and meaningful way, whereas 52% (33/63) indicated that they would definitely share it with others, reflecting strong engagement. However, over half (33/63, 52%) also found the amount of information excessive, and 17% (11/63) found it difficult to digest, indicating variation in youth information preferences. Thematic analysis of qualitative feedback revealed four key themes: (1) the visual content enabled gaining new insights into and knowledge of vaping, (2) the visual design had a positive impact on engagement with information, (3) sourced information enhanced the credibility of the infographic information, and (4) the digital design of the infographic made complex information more understandable. Qualitative insights contextualized and supported the quantitative findings, highlighting both benefits and areas for improvement. CONCLUSIONS This study demonstrates that youth-driven digital infographics may serve as useful health communication tools. Findings highlight the importance of peer-led design; evidence-based content; and interactive, visually compelling formats in enhancing youth comprehension and receptiveness to health messaging. By integrating youth feedback into development and prioritizing digital engagement, the infographic bridged knowledge gaps while reinforcing the credibility of its content. Variability in feedback about content overload suggests that future versions should consider more layered or modular designs. Results suggest that such approaches may complement broader public health strategies to curb youth vaping and inform future educational interventions. Continued research is warranted to assess long-term impacts on attitudes and behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".