Attitudes Toward the Global Allocation of Chinese COVID-19 Vaccines: Cross-sectional Online Survey of Adults Living in China
Bibliographic record
Abstract
BACKGROUND: COVID-19 vaccines are in short supply worldwide. China was among the first countries to pledge supplies of the COVID-19 vaccine as a global public product, and to date, the country has provided more than 600 million vaccines to more than 200 countries and regions with low COVID-19 vaccination rates. Understanding the public's attitude in China toward the global distribution of COVID-19 vaccines could inform global and national decisions, policies, and debates. OBJECTIVE: The aim of this study was to determine the attitudes of adults living in China regarding the global allocation of COVID-19 vaccines developed in China and how these attitudes vary across provinces and by sociodemographic characteristics. METHODS: We conducted a cross-sectional online survey among adults registered with the survey company KuRunData. The survey asked participants 31 questions about their attitudes regarding the global allocation of COVID-19 vaccines developed in China. We disaggregated responses by province and sociodemographic characteristics. All analyses used survey sampling weights. RESULTS: A total of 10,000 participants completed the questionnaire. Participants generally favored providing COVID-19 vaccines to foreign countries before fulfilling domestic needs (75.6%, 95% CI 74.6%-76.5%). Women (3778/4921, 76.8%; odds ratio 1.18, 95% CI 1.07-1.32; P=.002) and those living in rural areas (3123/4065, 76.8%; odds ratio 1.13, 95% CI 1.01-1.27; P=.03) were especially likely to hold this opinion. Most respondents preferred providing financial support through international platforms rather than directly offering support to individual countries (72.1%, 95% CI 71%-73.1%), while for vaccine products they preferred direct provision to relevant countries instead of via a delivery platform such as COVAX (77.3%, 95% CI 76.3%-78.2%). CONCLUSIONS: Among our survey sample, we found that adults are generally supportive of the international distribution of COVID-19 vaccines, which may encourage policy makers to support and implement the distribution of COVID-19 vaccines developed in China worldwide. Conducting similar surveys in other countries could help align policy makers' actions on COVID-19 vaccine distribution with the preferences of their constituencies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".