“Let’s get back to normal”: emotions mediate the effects of persuasive messages on willingness to vaccinate for COVID-19
Bibliographic record
Abstract
Objective: We examined the effectiveness of three different messages for persuading individuals to get vaccinated against COVID-19, and the role that emotions play in persuasion. Methods: Four hundred-thirty-six participants reported their concern about the COVID-19 pandemic and confidence/hesitancy toward vaccines. Participants were randomly assigned to one of three text conditions: (1) self-interest: a persuasive message that focused on how much of a "serious threat COVID-19 is to you," and to get vaccinated to "protect yourself"; (2) self-interest + altruistic: a persuasive message that focused on the "threat to you and your community" and to get vaccinated to "protect you and your loved ones"; (3) self-interest + altruistic + normal: a persuasive message that included (2) but added "This is the only way we can get back to a normal life."; and, (4) a baseline control: no text. After reading, participants reported their emotions toward COVID-19 vaccines and their willingness to get vaccinated. Results: Individuals in the self-interest + altruistic + normal condition were more willing to get vaccinated compared to the control condition and self-interest + altruistic condition. However, there were no differences in willingness between the self-interest + altruistic + normal condition and the self-interest condition. Moreover, emotions mediated relations between vaccine confidence/hesitancy and willingness. Conclusion: A message that focuses on "getting back to normal" can achieve important public health action by increasing vaccine uptake to protect the population. Future work is needed across multiple countries and contexts (i.e., non-pandemic) to assess message effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".