Psychological Factors Affecting Risk Perception of COVID-19: Evidence from Peru and China
Bibliographic record
Abstract
COVID-19 has spread around the world, causing a global pandemic, and to date is impacting in various ways in both developed and developing countries. We know that the spread of this virus is through people’s behavior despite the perceived risks. Risk perception plays an important role in decision-making to prevent infection. Using data from the online survey of participants in Peru and China (N = 1594), data were collected between 8 July 31 and August 2020. We found that levels of risk perception are relatively moderate, but higher in Peru compared to China. In both countries, anxiety, threat perception, self-confidence, and sex were found to be significant predictors of risk perception; however, trust in the information received by government and experts was significant only in Peru, whereas self-confidence had a significant negative effect only for China. Risk communication should be implemented through information programs aimed at reducing anxiety and improving self-confidence, taking into consideration gender differences. In addition, the information generated by the government should be based on empirical sources. Finally, the implications for effective risk communication and its impacts on the health field are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".