Association of COVID-19 Risk Misperceptions With Household Isolation in the United States: Survey Study
Bibliographic record
Abstract
BACKGROUND: Adverse mental and emotional health outcomes are increasingly recognized as a public health challenge associated with the COVID-19 pandemic. OBJECTIVE: The goal of this study was to examine the association of COVID-19 risk misperceptions with self-reported household isolation, a potential risk factor for social isolation and loneliness. METHODS: We analyzed data from the Franklin Templeton-Gallup Economics of Recovery Study (July to December 2020) of 24,649 US adults. We also analyzed data from the Gallup Panel (March 2020 to February 2021), which included 123,516 observations about loneliness. The primary outcome was self-reported household isolation, which we defined as a respondent having no contact or very little contact with people outside their household, analogous to quarantining. RESULTS: From July to December 2020, 53% to 57% of respondents reported living in household isolation. Most participants reported beliefs about COVID-19 health risks that were inaccurate, and overestimation of health risk was most common. For example, while deaths in persons younger than 55 years old accounted for 7% of total US deaths, respondents estimated that this population represented 43% of deaths. Overestimating COVID-19 health risks was associated with increased self-reported household isolation, with percentage differences ranging from 5.6 to 11.8 (P<.001 at each time point). Characteristics associated with self-reported household isolation from the July and August 2020 surveys and persisting in the December 2020 survey included younger age (18 to 39 years), having a serious medical condition, having a household member with a serious medical condition, and identifying as a Democrat. In the Gallup Panel, self-reported household isolation was associated with a higher prevalence of loneliness. CONCLUSIONS: Pandemic-related harms to emotional and mental well-being may be attenuated by reducing risk overestimation and household isolation preferences that exceed public health guidelines.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".