Leveraging Canadian healthcare worker volunteers to address COVID-19 vaccine misinformation on Facebook: A qualitative program evaluation (Preprint)
Notice bibliographique
Résumé
BACKGROUND Social media is currently serving 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. OBJECTIVE (1) Train and deploy a group of healthcare workers and student volunteers to address anti-vaccine sentiment on Facebook; and, (2) Evaluate the intervention through semi-structured interviews to determine lessons learned and suggestions for future initiatives to address misinformation online METHODS The project utilized volunteers to address vaccine-hesitant comments on Facebook (Met Platforms Inc., Menlo Park, California), with the overall goal of empowering healthcare professionals to engage with vaccine-hesitant individuals online to counteract the spread of vaccine misinformation. Eligible participants included healthcare workers and students in healthcare-related disciplines were recruited through social media and email advertising campaigns by the University of Calgary, School of Nursing contact list. Informational training sessions on Zoom with a duration of 30-minutes followed, to better equip volunteers with the ability to utilize their working knowledge of health communication and behaviour change to correct online misinformation. During the deployment of volunteers, they 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 in the project were provided with working knowledge of health communication, behaviour change, and correct misinformation through the informational training sessions. Qualitative evaluation in the form of interviews were used to examine participant experiences, where it was found that volunteers felt that they were adequately equipped to engage in vaccine conversations both online and in healthcare settings. Overall, the project has addressed vaccine hesitancy and valuable insights into the relationship between public engagement and communication in the era of digital interconnectedness. RESULTS Following the evaluative interview discussions, three main themes emerged regarding the project’s format and training model, the factors motivating volunteers to participate, and overall experiences tackling misinformation on an online 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. Finally, 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 healthcare professionals and students while engaging in vaccine-related conversations online. Immediate challenges associated with the COVID-19 pandemic, including combating misinformation and promotion of vaccination were addressed, which has resulted in a more reliable groundwork for shaping future public health communication strategies. To enhance both participant satisfaction and compliance, future interventions should consider utilizing a self-paced format, flexible hours, and highlight the vitality of healthcare professionals as key advocates for trusted sources of information for the public.
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 | 0,042 | 0,043 |
| 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 | 0,011 | 0,005 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».