A double-shot against COVID-19? Hope for a vaccine increases mitigation intentions and emotional well-being by reducing fatalism
Bibliographic record
Abstract
This research assessed the causal effect of hope for a COVID-19 vaccine on behavioral intentions to support mitigation efforts (e.g., social distancing) and emotional well-being during the coronavirus pandemic. Two studies (N=1297) tested the hypothesis that experimentally manipulated hope for a vaccine would increase mitigation intentions and emotional well-being by reducing fatalism toward COVID-19. In Study 1, hope for the vaccine manipulated with an article that promoted (vs. undermined vs. control) the likelihood that a vaccine would be developed to stop the pandemic. In Study 2, hope was manipulated with real news emerging from vaccine developers indicating that two vaccines highly effective and were nearing ready. Both manipulations reduced COVID-19 fatalism and thereby increased mitigation intentions and emotional well-being. Study 2 furthermore found that positive vaccine news increased people’s intentions to get vaccinated. Implications for COVID-19 mitigation efforts and mental health in the face of the coronavirus pandemic are discussed.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".