Ending the Pandemic: How Behavioural Science Can Help Optimize Global COVID-19 Vaccine Uptake
Bibliographic record
Abstract
Governments, public health officials and pharmaceutical companies have all mobilized resources to address the COVID-19 pandemic. Lockdowns, social distancing, and personal protective behaviours have been helpful but have shut down economies and disrupted normal activities. Vaccinations protect populations from COVID-19 and allow a return to pre-pandemic ways of living. However, vaccine development, distribution and promotion have not been sufficient to ensure maximum vaccine uptake. Vaccination is an individual choice and requires acceptance of the need to be vaccinated in light of any risks. This paper presents a behavioural sciences framework to promote vaccine acceptance by addressing the complex and ever evolving landscape of COVID-19. Effective promotion of vaccine uptake requires understanding the context-specific barriers to acceptance. We present the AACTT framework (Action, Actor, Context, Target, Time) to identify the action needed to be taken, the person needed to act, the context for the action, as well as the target of the action within a timeframe. Once identified a model for identifying and overcoming barriers, called COM-B (Capability, Opportunity and Motivation lead to Behaviour), is presented. This analysis identifies issues associated with capability, opportunity and motivation to act. These frameworks can be used to facilitate action that is fluid and involves policy makers, organisational leaders as well as citizens and families.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".