Preferred Information Source Correlates to COVID-19 Risk Misperception
Bibliographic record
Abstract
Inaccurate perceptions of COVID-19 (coronavirus disease 2019) risk may decrease compliance with public health mitigation practices, in turn increasing disease burden. The extent to which public perceptions of COVID-19 risk are inaccurate is not well studied. This study investigates the relationship between preferred information sources and inaccurate COVID-19 risk perception. A cross-sectional online survey of adults in the United States using online snowball techniques was administered between April 9, 2020 and July 12, 2020. Raking techniques were used to generate a representative U.S. sample from 10,650 respondents. Respondents who did not provide an answer to key questions were excluded. The remaining sample included 1,785 health care workers (HCW) and 4,843 non-HCW. Subjective risk was measured as the product of perceived likelihood of COVID-19 infection and perceived harm from infection. Objective risk was measured as a function of the presence of known COVID-19 risk factors. Discrepancies between subjective and objective risk were compared between respondents with different preferred information sources. Chi Square contingency tables and pair-wise correlation were used to evaluate differences to 95% confidence. For HCW and non-HCW, the greatest overestimation of personal COVID-19 risk assessment ( p < .05 for all differences) were found in those whose preferred source of information was social media (HCW: 62.1%; non-HCW: 64.5%), followed by internet news sources (HCW: 59.6%, non-HCW%: 59.1%), government websites (HCW: 54%, non-H CW = 51.8%), other sources (HCW: 50.7%, non-H CW = 51.4%), and television news (HCW: 46.1%, non-HCW: 47.6%). Preferred information sources correlate with inaccuracies in personal COVID-19 risk assessment. Public health information campaigns should consider targeting groups whose preferred information sources correlate to higher inaccuracies in COVID-19 risk perceptions. [ HLRP: Health Literacy Research and Practice . 2023;7(2):e105–e110. ]
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".