Assessing WHO’s influence: A randomized conjoint experiment on vaccine endorsements in diversified global health systems
Bibliographic record
Abstract
During a novel pandemic, significant uncertainty drives individuals to seek expert guidance on preventive measures such as vaccination. Yet, it remains poorly understood how people process information in a highly complex landscape of global health governance where multiple experts may offer competing, repetitive, or contradictory advice. This study investigates the influence of World Health Organization (WHO)'s endorsements of vaccines amidst this environment. In fall 2020, we conducted a randomized conjoint experiment in Canada (832 respondents, 8,320 profiles evaluated), Japan (1,474, 14,740), and the United States (1,001, 10010), focusing on both whether and when people choose to vaccinate against COVID-19. Our experiment randomly varied exposure to vaccine endorsement information from several prominent global health governance players, including the WHO, the Centers for Disease Control and Prevention (CDC), Oxford University, and the Gates Foundation; and, unlike previous studies, different combinations of these endorsements were used. WHO endorsements increase individuals' willingness to vaccinate more quickly, even when accompanied by endorsements from other credible organizations. However, the effect of WHO endorsements is not significantly stronger than that of other organizations. Notably, the impact of the WHO's endorsement diminishes as the number of endorsements from other organizations increases. The WHO has the greatest impact when it is the first (or among the first) of many organizations to endorse a vaccine as safe and effective, and it may help inspire public confidence in less effective (but potentially lifesaving) vaccines. Overall, our study shows that WHO endorsements significantly reduce vaccine hesitancy, but endorsements from other global actors can exert comparable effects. This highlights that effective global health communication thus depends not on a single authoritative voice but on the timely coordination of multiple credible actors, underscoring the resilience of the global health system in promoting vaccine acceptance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".