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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".