Keeping up with COVID‐19 information: Capacity issues and knowledge uncertainty early in the pandemic
Bibliographic record
Abstract
This article examines the relationship between information consumption and mental health during the early stages of the COVID-19 pandemic. Adopting a qualitative approach, we interviewed 39 people in British Columbia, Canada between October and December 2020. Interestingly, half of the participants did not want to seek out new information on COVID-19, making their early insights and initial confusion salient. While some individuals did desire up-to-date information on outbreaks and new risks, many expressed confusion over what was perceived to be an evolving landscape of public health policy and practice. Overall, our research found that capacity issues, information overload/fatigue, politics, distrust, and competing sources of news all contributed to a culture of confusion towards public health information. As a consequence, this confusion resulted in knowledge uncertainty about the virus, vaccinations, and the pandemic itself. Our findings highlight the need for a host of future projects that examine how citizens experience disempowerment and limited agency towards compliance with health and safety initiatives.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".