A brief forewarning intervention overcomes negative effects of salient changes in COVID-19 guidance
Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, public health guidance (e.g., regarding the use of non-medical masks) changed over time. Although many revisions were a result of gains in scientific understanding, we nonetheless hypothesized that making changes in guidance salient would negatively affect evaluations of experts and health-protective intentions. In Study 1 ( N = 300), we demonstrate that describing COVID-19 guidance in terms of inconsistency (versus consistency) leads people to perceive scientists and public health authorities less favorably (e.g., as less expert). For participants in Canada ( n = 190), though not the U.S. ( n = 110), making guidance change salient also reduced intentions to download a contact tracing app. In Study 2 ( N = 1399), we show that a brief forewarning intervention mitigates detrimental effects of changes in guidance. In the absence of forewarning, emphasizing inconsistency harmed judgments of public health authorities and reduced health-protective intentions, but forewarning eliminated this effect.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".