Scientists, speak up! Source impacts trust in health across five countries
Bibliographic record
Abstract
We examined how different types of communication influence people’s responses to health advice. We tested whether presenting Covid-19 prevention advice (e.g., washing hands/distancing) as either originating from a government or scientific source would affect people’s trust in and intentions to comply with the advice. We also manipulated uncertainty in communicating the advice effectiveness. To achieve this, we conducted an experiment using large samples of participants (N= 4,561) from the UK, US, Canada, Malaysia, and Taiwan. Across countries, participants found messages more trustworthy when the purported source was science rather than the government. This effect was moderated by political orientation in all countries except for Canada, while religiosity moderated the source effect in the US. Although source did not directly affect intentions to act upon the advice, we found an indirect effect via trust, such that a more trusted source (i.e., science) was predictive of higher intentions to comply. However, the uncertainty manipulation was not effective. Together, our findings suggest that despite prominence of science scepticism in public discourse, people trust scientists more than governments when it comes to practical health advice. It is therefore beneficial to communicate health messages by stressing their scientific bases.
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".