Leveraging Canadian Health Care Worker Volunteers to Address COVID-19 Vaccine Misinformation on Facebook: Qualitative Program Evaluation Study
Notice bibliographique
Résumé
Background: Social media serves as a tool for increased digital interconnectedness and has resulted in playing an instrumental role in sharing health-related information with a wide audience. In conjunction with the vast availability of information, there has been a rapid spread of misinformation, leading to public mistrust, safety concerns, and discrimination. The COVID-19 pandemic has amplified the threat of misinformation resulting in detrimental health outcomes due to individuals becoming fatigued with COVID-19 health guidance. Although vaccinations are the key to combating COVID-19, the overwhelming amount of misinformation has resulted in diminished vaccine acceptance. Unlabelled: This study aims to (1) train and deploy a group of health care workers and student volunteers to address antivaccine sentiment on Facebook (Meta Platforms, Inc) and (2) evaluate the intervention through semistructured interviews to determine lessons learned and suggestions for future initiatives to address internet-based misinformation online. Methods: The project used volunteers to address vaccine-hesitant comments on Facebook (Meta Platforms, Inc), with the overall goal of empowering health care professionals to counteract the spread of vaccine misinformation. Eligible participants included health care workers and students in health care-related disciplines recruited through social media and email advertising campaigns. Informational training sessions followed, to better equip volunteers with the ability to use their working knowledge of health communication and behavior change to correct web-based misinformation. The volunteers were provided a file containing Facebook posts that discussed COVID-19 vaccines to act as a starting point for leaving or responding to comments that spread vaccine misinformation. Participants were provided with working knowledge of health communication, behavior change, and correct misinformation through the informational training sessions. Qualitative evaluation in the form of interviews was used to examine participant experiences. Results: Three main themes emerged regarding the project's format and training model, the factors motivating volunteers to participate, and overall experiences tackling misinformation on a social media platform. The first theme showcased that the training format was effective due to its use of interactive components and overall flexibility, resulting in it being well received by volunteers. The second identified theme highlighted that a main driving factor for participation included a balance of professional development and societal good. The third theme revealed that the volunteers' experiences in interacting with the public revealed a rich tapestry of emotions and perspectives, where vaccine hesitancy is interconnected with emotional responses and personal beliefs. Conclusions: The Informed Choice Project provided an opportunity to increase self-efficacy and confidence for more than a dozen health care professionals and students while engaging in vaccine-related conversations on social media. To enhance both participant satisfaction and compliance, future interventions should consider using a self-paced format, flexible hours, and highlight the vitality of health care professionals as key advocates for trusted sources of information for the public.
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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 | 0,037 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,012 | 0,005 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».