Factors Associated with the Usefulness of Public Health Communication in the Context of COVID-19: Lessons Learned from the African, Caribbean, and Black Communities in Ottawa, Ontario
Bibliographic record
Abstract
Public health communication is critical for promoting behaviours that can prevent the transmission of COVID-19. However, there are concerns about the effectiveness of public health communication within Canada’s African, Caribbean, and Black (ACB) communities. In the community sample of ACB people in Ottawa, Ontario, we asked community members if they perceive public health message related to COVID-19 to be effective. Using this question, the current study aimed to explore factors associated with the perceived usefulness of public health messages related to COVID-19. Results from the multivariate analysis have shown that ACB people with lower levels of risk perception for COVID-19 were less likely to perceive that public health messages were useful (OR = 0.405, p < 0.01). In addition, mistrust in government COVID-19 information was also negatively associated with their perception that health messages are useful (OR = 0.169, p < 0.01). For socioeconomic status, ACB people with no high school diploma (OR = 0.362, p < 0.05) and income dissatisfaction (OR = 0.431, p < 0.05) were less likely to report the perceived usefulness compared to those with a bachelor’s degree and income satisfaction. Based on these findings, we discussed implications for policymakers and directions for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".