Pandemic Fatigue and Preferences for COVID-19 Public Health and Social Measures in China: Nationwide Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Information on the public's preferences for current public health and social measures (PHSMs) and people's mental health under PHSMs is insufficient. OBJECTIVE: This study aimed to quantify the public's preferences for varied PHSMs and measure the level of pandemic fatigue in the COVID-19 normalization stage in China. METHODS: A nationwide cross-sectional study with a discrete choice experiment and psychometric scales was conducted to assess public preferences for and attitudes toward PHSMs, using the quota sampling method. The COVID-19 Pandemic Fatigue Scale (CPFS) was used to screen fatigue levels among respondents. The multinomial logit model, latent class model, and Mann-Whitney test were used for statistical analysis. We also conducted subgroup analysis based on sex, age, monthly income, mental health status, and pandemic fatigue status. RESULTS: A total of 689 respondents across China completed the survey. The discrete choice experiment revealed that respondents attached the greatest importance to the risk of COVID-19 infection within 3 months (45.53%), followed by loss of income within 3 months (30.69%). Vulnerable populations (low-income populations and elderly people) were more sensitive to the risk of infection, while younger respondents were more sensitive to income loss and preferred nonsuspension of social places and transportation. Migrants and those with pandemic fatigue had less acceptance of the mandatory booster vaccination and suspension of transportation. Additionally, a higher pandemic fatigue level was observed in female respondents, younger respondents, migrants, and relatively lower-income respondents (CPFS correlation with age: r=-0.274, P<.001; correlation with monthly income: r=-0.25, P<.001). Mandatory booster COVID-19 vaccination was also not preferred by respondents with a higher level of pandemic fatigue, while universal COVID-19 booster vaccination was preferred by respondents with a lower level of pandemic fatigue. CONCLUSIONS: Pandemic fatigue is widely prevalent in respondents across China, and respondents desired the resumption of normal social life while being confronted with the fear of COVID-19 infection in the normalization stage of COVID-19 in China. During future pandemics, the mental burden and adherence of residents should be considered for the proper implementation of PHSMs.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".