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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".