Lessons from COVID-19 for behavioural and communication interventions to enhance vaccine uptake
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although the COVID-19 pandemic is widely considered to be over, vaccination remains the crucial tool to protect people from severe disease. Notwithstanding adequate supply, vaccine uptake varies considerably among countries and segments of society. For example, as of 30 June 2023, uptake of the primary course of vaccines in Europe ranged from 21.1% in Kyrgyzstan to 92.6% in Spain, and in the U.S. uptake is far higher among Democrats than Republicans with the gap exceeding 30% in some surveys. There were many reasons for low uptake, varying from country to country; however, a sizeable number of people across the globe chose not to get vaccinated. This hesitancy, much of it propelled by disinformation, has also spilled over into childhood vaccinations, with a notable decrease in confidence in 52 out of 55 countries polled by the United Nations International Children’s Emergency Fund (UNICEF). Evidence-informed strategies for addressing low vaccine uptake are thus urgently required.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it