Analyzing the use of X to communicate vaccine rollout in Region of Peel (Ontario, Canada): A Multimethod Approach (Preprint)
Notice bibliographique
Résumé
BACKGROUND Social media can facilitate community engagement and promote public trust but also fuel public mistrust. In the early stages of the COVID-19 pandemic, some areas in Ontario were identified as COVID-19 hotspots, where infection rates were higher than the provincial average. Several hotspots were comprised of neighborhoods with ethnoracially minoritized populations who are often precariously employed and housed, making them more vulnerable to acquiring COVID-19. OBJECTIVE This study aimed to characterize and assess the ways in which social media was used by public health officials and community partners to reach communities and build vaccine confidence in Region of Peel. METHODS A multimethod approach was employed to examine vaccine-related communication practices on social media platform X (formerly Twitter). Three methods—trend analysis, qualitative content analysis, and descriptive analysis were used. Data collection was conducted from December 2020 to November 2021 using Brandwatch™ to collect and annotate COVID-19 related tweets from Region of Peel and its 129 Mass Vaccination Program (MVP) partners throughout three phases of the vaccine rollout. To determine how Region of Peel used X during vaccine rollout, 24,637 tweets were automatically annotated to identify its lead agency, the vaccine locations that the tweets were promoting, the tweets’ intended priority populations, and whether tweets contained tailored messages to faith-based communities. RESULTS Peel utilized X as a medium for public service announcements (PSAs) to provide real-time information to the general public. Peel and its MVP partners (e.g., community health centers, other public sector and non-profit organizations) primarily focused on creating tweets to engage Indigenous and Black communities. The main themes addressed public questions about vaccine efficacy, safety, and eligibility per provincial guidelines, booking procedures, and vaccine availability. Black and South Asian community-based organizations (CBOs) were Peel’s most active partners on X. However, partners serving Punjabi and Hindu communities had limited visibility on X. Among faith-oriented CBOs, Muslim organizations were the most prominent in sharing religious messaging. CONCLUSIONS Peel leveraged existing relationships with partners to engage a broad audience about COVID-19 vaccines; its use of X demonstrated how strategic communication and partnerships can improve accessibility to vaccine information to ethnoracially minoritized and faith-based groups. The findings underscore the value of integrating social media with broader public health strategies to build trust and enhance engagement of diverse populations.
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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».