A content analysis of government-issued social media posts during multi-jurisdictional enteric illness outbreaks in Canada
Notice bibliographique
Résumé
Introduction Most Canadians use at least one social media platform regularly, making social media a potentially effective tool for reaching broad audiences. The Public Health Agency of Canada (PHAC) uses social media as one tool for rapidly communicating with the public during multi-jurisdictional enteric illness outbreaks. However, the effectiveness of social media in enhancing public risk communication during these outbreaks remains unexplored. Addressing this gap may help optimise social media use for risk communication to inform the public and prevent additional illness. This study aims to analyse the engagement with and quality of PHAC’s social media content regarding multi-jurisdictional enteric illness outbreaks. Methods Using a search of PHAC’s social media platforms, 482 posts during enteric illness outbreaks (2014–2022) were identified, including 198 posts from Facebook and 284 posts from X (formerly Twitter) in English and French. A codebook was developed using engagement metrics for gauging public interest, the Centers for Disease Control and Prevention’s (CDC) Modified Clear Communication Index (CCI) to assess clarity as a proxy for comprehension, the Health Belief Model (HBM) to evaluate the potential to motivate behaviour change, and measures of consistency. Descriptive statistics were used to analyse post content. Results The average engagement rates for PHAC social media accounts were < 1%, below standard average engagement rates (1–5%). While posts generally adhered to the CDC’s CCI criteria, clear language (45.7% on Facebook, 26.5% on X) and clear communication of risk (7.6% on Facebook, 0.0% on X) were scarce. HBM constructs were present in all posts, but certain constructs, such as barriers were used sparingly (1% on Facebook, 0% on X). Despite this, posts consistently communicated outbreak investigation details and prevention information. Discussion The low average engagement rates suggest a lack of public awareness or interest in the posts. The partial adherence to the CCI indicates room for improvement in clarity, a key component for supporting public understanding. Although some HBM constructs were utilised, no posts incorporated all HBM constructs, which may hinder efforts to promote behaviour change. To enhance effective risk communication using social media during multi-jurisdictional enteric illness outbreaks in Canada, tools like the CDC’s CCI should be used to improve message clarity, use of all HBM constructs as applicable, and message consistency across products and channels are recommended to improve overall message quality and content.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».