Factors associated with the opposition to COVID-19 vaccination certificates: A multi-country observational study from Asia
Bibliographic record
Abstract
BACKGROUND: There are ongoing calls to harmonise and increase the use of COVID-19 vaccination certificates (CVCs) in Asia. Identifying groups in Asian societies who oppose CVCs and understanding their reasons can help formulate an effective CVCs policy in the region. However, no formal studies have explored this issue in Asia. METHOD: The COVID-19 Vaccination Policy Research and Decision-Support Initiative in Asia (CORESIA) was established to address policy questions related to CVCs. An online cross-sectional survey was conducted from June to October 2021 in nine Asian countries. Multivariable logistical regression analyses were performed to identify potential opposers of CVCs. RESULTS: Six groups were identified as potential opposers of CVCs: (i) unvaccinated (Odd Ratio (OR): 2.01, 95% Confidence Interval (CI): 1.65-2.46); vaccine hesitant and those without access to COVID-19 vaccines; (ii) those not wanting existing NPIs to continue (OR: 2.97, 95% CI: 2.51-3.53); (iii) those with low level of trust in governments (OR: 1.25, 95% CI: 1.02-2.52); (iv) those without travel plans (OR: 1.58, 95% CI: 1.31-1.90); (v) those expecting no financial gains from CVCs (OR: 2.35, 95% CI: 1.98-2.78); and (vi) those disagreeing to use CVCs for employment, education, events, hospitality, and domestic travel. CONCLUSIONS: Addressing recurring public health bottlenecks such as vaccine hesitancy and equitable access, adherence to policies, public trust, and changing the narrative from 'societal-benefit' to 'personal-benefit' may be necessary and may help increase wider adoption of CVCs in Asia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".