Examining public health risk communication via social media by provincial and local health authorities in Ontario during the COVID-19 pandemic
Bibliographic record
Abstract
Risk communication campaigns are essential during public health crises to inform the public about ways to mitigate, alleviate and manage potential risks. The purpose of this study was to describe risk communication on social media by Ontarian health authorities amid COVID-19, in addition to examining the strategies that guided their social media use. This was completed through (a) a narrative review of risk communication literature; (b) a qualitative content analysis of select health authority Twitter messaging following three major COVID-19 milestones; and (c) key informant interviews with those coordinating social media responses to COVID-19. Information giving and news updates were the prominent functions of Twitter, while communicating about health equity and misinformation was less prominent. Interviews revealed that staffing, financial resources, and leadership buy-in are key to facilitating risk communication, and there is mixed use of theory and evidence to inform strategies. Recommendations are discussed, including the need for evidence-based, proactive emergency communication plans, and an increased consideration of equity in risk communications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".