Effects of Prosocial and Hope-Promoting Communication Strategies on COVID-19 Worry and Intentions for Risk-Reducing Behaviors and Vaccination: Experimental Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has engendered widespread fear and skepticism about recommended risk-reducing behaviors including vaccination. Health agencies are faced with the need to communicate to the public in ways that both provide reassurance and promote risk-reducing behaviors. Communication strategies that promote prosocial (PS) values and hope are being widely used; however, the existing research on the persuasiveness of these strategies has offered mixed evidence. There is also very little research examining the comparative effectiveness of PS and hope-promoting (HP) strategies. OBJECTIVE: The aim of this study is to evaluate the comparative effectiveness of PS and HP messages in reassuring the public and motivating COVID-19 risk-reducing behaviors. METHODS: A web-based factorial experiment was conducted in which a diverse sample of the US public was randomized to read messages which adapted existing COVID-19 information from a public website produced by a state government public health department to include alternative framing language: PS, HP, or no additional framing (control). Participants then completed surveys measuring COVID-19 worry and intentions for COVID-19 risk-reducing behaviors and vaccination. RESULTS: COVID-19 worry was unexpectedly higher in the HP than in the control and PS conditions. Intentions for COVID-19 risk-reducing behaviors did not differ between groups; however, intentions for COVID-19 vaccination were higher in the HP than in the control condition, and this effect was mediated by COVID-19 worry. CONCLUSIONS: It appears that HP communication strategies may be more effective than PS strategies in motivating risk-reducing behaviors in some contexts but with the paradoxical cost of promoting worry.
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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 | 0.002 | 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 | 0.001 | 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".