Novel strategies to support global promotion of COVID-19 vaccination
Bibliographic record
Abstract
In 2021, many countries have begun distribution of COVID-19 vaccines but are hampered by significant levels of vaccine hesitancy or apathy. Experts recommend that standard health communication campaigns be expanded to include a more holistic approach of behaviourally oriented strategies. We constructed a large-scale Delphi panel of marketing and behavioural science university faculty to assess 12 previously reported US vaccination promotion strategies, asking respondents to assess applicability of the strategy in their country, how efficacy might compare to the USA and recommendations for local adaptations necessary to successful implementation. Separately, we sought to determine whether strategies based on cognitive mechanisms (eg, 'nudges') are more readily generalisable than strategies based on social identity. Ninety-two marketing and behavioural science faculty from universities worldwide participated. Globally, all 12 behavioural strategies were validated; a majority of respondents reported that they would or could work well in their country. While all strategies were strongly validated at a global level, specific need for regional adaptation was identified. Also, open-ended responses suggested the addition of three emergent strategies to a global effort. Finally, we see that strategies based on some types of cognitive mechanisms are more readily generalisable across regions than mechanisms based on social identity, however, this is not always true of 'nudge' strategies. All 12 strategies are robust to global use and consensus exists on adaptation for optimal efficacy in different regions; specific strategy recommendations are posited. Use of these strategies can accelerate individual country efforts to achieve desired vaccination rates to protect global public health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".