Risk Perception in a Real-World Situation (COVID-19): How It Changes From 18 to 87 Years Old
Bibliographic record
Abstract
Studies on age-related differences in risk perception in a real-world situation, such as the recent COVID-19 outbreak, showed that the risk perception of getting COVID-19 tends to decrease as age increases. This finding raised the question on what factors could explain risk perception in older adults. The present study examined age-related differences in risk perception in the early stages of COVID-19 lockdown, analyzing variables that can explain the differences in perception of risk at different ages. A total of 1,765 adults aged between 18 and 87 years old completed an online survey assessing perceived risk severity and risk vulnerability of getting COVID-19, sociodemographic status, emotional state, experience relating to COVID-19, and physical health status. Results showed that the older the participants, the lower the perceived vulnerability to getting COVID-19, but the higher the perceived severity. Different predictors explain the perception of risk severity and vulnerability at different ages. Overall, self-reported anxiety over the pandemic is a crucial predictor in explaining risk perceptions in all age groups. Theoretical and practical implications of the empirical findings are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".