Canadian public perceptions and experiences with information during the COVID-19 pandemic: strategies to optimize future risk communications
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic accelerated the spread of misinformation worldwide. The purpose of this study was to explore perceptions of misinformation and preferred sources of obtaining COVID-19 information from those living in Canada. In particular, we sought to explore the perceptions of East Asian individuals in Canada, who experienced stigma related to COVID-19 messaging. METHODS: We conducted a qualitative thematic analysis study. Interviews were offered in English, Mandarin and Cantonese. Interviewers probed for domains related to knowledge about COVID-19, preferred sources of information, perceived barriers and facilitators of misinformation, and preferences for communication during a health emergency. Interviews were recorded, translated, transcribed verbatim and analyzed using a framework approach. Transcripts were independently double-coded until > 60% agreement was reached. This study received research ethics approval. RESULTS: Fifty-five interviews were conducted. The majority of participants were women (67%); median age was 52 years. 55% of participants were of East-Asian descent. Participants obtained information about COVID-19 from diverse English and non-English sources including news media, government agencies or representatives, social media, and personal networks. Challenges to seeking and understanding information included: encountering misinformation, making sense of evolving or conflicting public health guidance, and limited information on topics of interest. 65% of participants reported encountering COVID-19 misinformation. East Asian participants called on government officials to champion messaging to reduce stigmatizing and racist rhetoric and highlighted the importance of having accessible, non-English language information sources. Participants provided recommendations for future public health communications guidance during health emergencies, including preferences for message content, information messengers, dissemination platforms and format of messages. Almost all participants preferred receiving information from the Canadian government and found it helpful to utilize various mediums and platforms such as social media and news media for future risk communication, urging for consistency across all platforms. CONCLUSIONS: We provide insights on Canadian experiences navigating COVID-19 information, where more than half perceived encountering misinformation on platforms when seeking COVID-19 information . We provide recommendations to inform public health communications during future health emergencies.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".