A Youth-Centered Digital Infographic on Vaping Risks (What’s in a Vape?): Mixed Methods Study (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».