To what extent do young chinese elites comply with COVID-19 prevention and control measures?
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, it is vital for individuals to comply with the government's prevention and control measures. This study aims to explore determinants of college students' compliance behaviour during the COVID-19 pandemic. METHODS: This study conducted an online survey among 3,122 individuals aged 18 and above from March to November 2022 in China. Individuals' compliance behaviour was divided into protective behaviour (that includes wearing a mask, maintaining a physical distance, and getting vaccinated) and restrictive behaviour (that includes offering health codes and a nucleic acid test certificate). Individuals' compliance motivation was divided into calculated motivation (including the fear of being infected, the fear of being published, and previous experience of pandemic prevention) and normative motivation (including the sense of social responsibility and trust in government). We defined young people aged between 18 and 24 with a college degree as young elites, and constructed ordinary least squares linear regression to compare their compliance behaviour with young people without a college degree (young non-elites), and non-young people with a college degree (non-young elites). RESULTS: Almost three years after the outbreak of the pandemic, Chinese individuals retained a high degree of compliance with COVID-19 prevention and control policies, particularly with respect to the provision of health codes. Young elites were more compliant with getting vaccinated, wearing a mask, providing health codes and testing results than their counterparts. The sense of social responsibility and trust in government were the major drivers of young elites' compliance behaviour during the pandemic. Young elites who were male, had a rural "hukou", and were not a member of the China Communist Party were more compliant with COVID-19 prevention and control measures. CONCLUSION: This study found that young elites in China had high policy compliance during the COVID-19 pandemic. These young elites' compliance behaviour was driven by their sense of social responsibility and trust in government rather than the fear of being infected and the fear of being punished as a result of violating the regulations. We suggest that in the context of managing health crises, in stead of introducing punitive measures to enforce citizens to comply with the management measures, promoting citizens' sense of social responsibility and building a trusting relationship with citizens contrite to the enhancement of citizens' policy compliance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".