Fear in general populations: A cross-sectional study on perceived fear of common diseases, COVID-19, life events, and environmental threats in 30 countries
Bibliographic record
Abstract
Background: In this study, we assessed the general population's fears towards various diseases and events, aiming to inform public health strategies that balance health-seeking behaviours. Methods: We surveyed adults from 30 countries across all World Health Organization (WHO) regions between July 2020 and August 2021. Participants rated their fear of 11 factors on an 11-point Likert scale. We stratified the data by age and gender and examined variations across countries and regions through multidimensional preference analysis. Results: Of the 16 512 adult participants, 62.7% (n = 10 351) were women. The most feared factor was the loss of family members, reported by 4232 participants (25.9%), followed by cancer (n = 2248, 13.7%) and stroke (n = 1416, 8.7%). The highest weighted fear scores were for loss of family members (mean (x̄) = 7.46, standard deviation (SD) = 3.04), cancer (x̄ = 7.00, SD = 3.09), and stroke (x̄ = 6.61, SD = 3.24). The least feared factors included animals/insects (x̄ = 3.72, SD = 2.96), loss of a mobile phone (x̄ = 4.27, SD = 2.98), and social isolation (x̄ = 4.83, SD = 3.13). Coronavirus disease 2019 (COVID-19) was the sixth most feared factor (x̄ = 6.23, SD = 2.92). Multidimensional preference analyses showed distinct fears of COVID-19 and job loss in Australia and Burundi. The other countries primarily feared loss of family members, cancer, stroke, and heart attacks; this ranking was consistent across WHO regions, economic levels, and COVID-19 severity levels. Conclusions: Fear of family loss can improve public health messaging, highlighting the need for bereavement support and the prevention of early death-causing diseases. Addressing cancer fears is crucial to encouraging the use of preventive services. Fear of non-communicable diseases remains high during health emergencies. Top fears require more resources and countries with similar concerns should collaborate internationally for effective fear management.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".