Is There Broad-Based Support in High-Income Countries for COVID-19 Vaccine Donation? Evidence from Seven Countries
Bibliographic record
Abstract
BACKGROUND: Many high-income countries (HICs) have now vaccinated a substantial proportion of their population against COVID-19. Many low-income countries (LICs) may need to wait until at least 2022 before even the most vulnerable 20% of their populations are vaccinated. Beyond ethical considerations, some redistribution of doses would reduce the risk of the emergence and spread of new variants and benefit the economy, both globally and in donor countries. However, the willingness of HIC governments to donate vaccine doses is likely to depend on public support. While previous work has indicated strong average levels of public support in HIC for donation, little is known about how broad-based this support is. OBJECTIVE: To investigate the extent to which support for donation holds across both pre-specified and exploratory subgroups. METHODS: From 24 November-28 December 2020 we conducted an online survey of 8209 members of the general public in seven HIC (Australia, Canada, France, Italy, Spain, UK and USA). We conducted tests of proportions and used Bayesian ordinal logistic regression models to assess the extent of support for donation across population subgroups. RESULTS: We found broad-based support for donations in terms of age, gender, socio-economic status and political ideology. We found no strong evidence that support for donations was higher among those with greater income or a university education. Support for donation among those on the political right and centre was lower than on the left, but 51% (95% confidence interval 48-53%) of respondents who identified with the right supported some level of donation. Those in the more altruistic half of the sample (as captured by willingness to donate money to a good cause) were more likely to support donation than those who were not, but around half of the less altruistic group supported some level of donation. CONCLUSION: There is broad-based support for policymakers in HICs to donate some of their countries' COVID-19 vaccine doses for distribution to LICs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".