Digital Storytelling Methods to Empower Young Black Adults in COVID-19 Vaccination Decision-Making: Feasibility Study and Demonstration
Notice bibliographique
Résumé
BACKGROUND: Despite high rates of novel COVID-19, acceptance of COVID-19 vaccination is low among Black adults. In response, we developed a digital health intervention (Tough Talks-COVID) that includes digital stories created in a workshop we held with young Black adults. OBJECTIVE: Our formative research using digital storytelling workshops asked 3 research questions: (1) What issues did participants have in conceptualizing their stories, and what themes emerged from the stories they created? (2) What issues did participants have related to production techniques, and which techniques were utilized in stories? and (3) Overall, how did participants evaluate their workshop experience? METHODS: Participants were workshop-eligible if they were vaccine-accepting based on a baseline survey fielded in late 2021. Final participants (N=11) completed a consent process, all 3 workshops, and a media release form for their digital story. The first 2 workshops provided background information and hands-on digital storytelling skills from pre- to postproduction. The third workshop served as a screening and feedback session for participants' final videos. Qualitative and quantitative feedback elements were incorporated into all 3 sessions. RESULTS: Digital stories addressed one or more of 4 broad themes: (1) COVID-19 vulnerability, (2) community connections, (3) addressing vaccine hesitancy, and (4) countering vaccine misinformation. Participants incorporated an array of technical approaches, including unique creative elements such as cartoon images and instant messaging tools to convey social interactions around COVID-19 decision-making. Most (9/11, 82%) strongly agreed the digital storytelling workshops were delivered as expected; 10 of 11 agreed (n=5) or strongly agreed (n=5) that they had some ideas about what story to tell by the end of the first workshop, and most (8/11, 73%) strongly agreed they had narrowed down their ideas by workshop two. Of the participants, 9 felt they would very likely (n=6) or likely (n=3) use digital storytelling techniques for personal use in the future, and even more were very likely (n=7) to use the techniques for professional use. CONCLUSIONS: Our study is one of the first to incorporate digital storytelling as a central component to a digital health intervention and the only one to do so with exclusive focus on young Black adults. Our emphasis on digital storytelling was shown to be highly acceptable. Similar approaches, including careful consideration of the ethical challenges of community-based participatory approaches, are applicable to other populations experiencing both COVID-19 inequities and marginalization, such as other age demographics and people of color.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».