Rapid discovery of optimal messages for behavioral intervention: the case of Hungary and Covid-19
Bibliographic record
Abstract
The right messaging plays an important role in the fight against the spread of COVID-19. The present study aims at uncovering the way people think about governmental measures against COVID-19. Two hundred and sixteen Hungarians participated in this on-line study. A conjoint-based experimental design was used to reveal the power of messages as drivers of voluntary social distancing based on the perceived risk of COVID-19, the ways to practice social distancing and to assure it, and preferences regarding the communicator of the social distancing policy. Results revealed three major mindsets: Pandemic observers, Order-followers, and Health-conscious. Members of each mindset respond differently to messages. To enhance compliance with social distancing and contain the virus, we suggest using the prediction tool we developed to identify the belonging of people or groups in the population to mindsets in the sample and address people using effective mindset-tailored messaging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".