A content analysis of government-issued social media posts during multi-jurisdictional enteric illness outbreaks in Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".