To what extent do college students cooperate with pandemic prevention and control policies? Compliance behaviours of young Chinese intellectual elites
Bibliographic record
Abstract
Abstract Background: During a crisis the COVID-19 pandemic, it is vital for young people, who always actively engage in more social activities, to comply with the government’s prevention and control measures. Methods: An online questionnaire was administered to people aged 18 years and above from mainland Chinese from February to May 2022. We used student's t test and chi-square tests to analyse the college students’ compliance behaviours. Compliance motivation was divided into calculated, motivation and social motivations. We compared young people’s compliance motivations across different age and education groups. Results: This study includes four key findings. First, the college students reported a high degree of compliance with COVID-19 pandemic prevention policies, especially those regarding obtaining vaccinations and providing codes or cards as a proof of health status, followed by wearing mask, taking nucleic acid tests, and maintaining a physical distance of at least 1 metre from every other person at public places. Second, older college students tended to comply with the pandemic prevention policies, while no significantly different variation was found among different education groups. Third, the primary motivations of the college students’ compliance were duties and obligations (77.5%), risk perception (63.7%), previous experience (56.7%), and trust in the government (52.1%); less important were bandwagon effect (5.4%), authoritative values (5%) and fear of being punished (2.8%). Fourth, compared with older college students, young college student were more likely to comply control policy because of government trust (52.1% vs 40.9%) and sense of responsibility (77.5% vs 72.7%), while older graduated students were more strongly motivated by risk perception (75.3% vs 63.7%) and past experience (61.8% vs 56.7%). Conclusion: Although college students’ compliance behaviour during the pandemic were motivated by a wide range of factors, our study identified that normative and calculated motivations were the most influential ones. Deterrence from calculated motivation and conformity from social motivation played only minor roles in impacting college students’ 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".