In the COVID-19 pandemic, who did we trust? An eight-country cross-sectional study
Bibliographic record
Abstract
Background: Trust is a key determinant of health, but has been undermined by the COVID-19 pandemic and the associated infodemic. Using data from eight countries, we aimed to epidemiologically describe levels of trust in health, governments, news media organisations, and experts, and measure the impact of political orientation and COVID-19 information sources on participant's levels of trust. Methods: We simultaneously conducted a stratified randomised online cross-sectional study across eight countries on adults aged ≥18 years between 6 and 18 November 2020. We employed crude and adjusted weighted regression analyses. Results: We included 9027 adults with a mean age of 47 years (range = 18-99), of whom 4667 (51.7%) were female. Trust in health experts ranked highest across all countries (mean (x̄) = 7.83; 95% confidence interval (CI) = 7.79-7.88), while trust in politicians ranked lowest (x̄ = 5.34; 95% CI = 5.28, 5.40). In adjusted analyses, political orientation and utilised information sources were significantly associated with trust. Individuals using higher levels of health information sources trusted health authorities more than those using lower levels (mean difference = 1.12; 95% CI = 1.02, 1.14). Similarly, individuals using higher levels of government information sources (mean difference = 1.55; 95% CI = 1.43, 1.64) and those using higher levels of new media information sources (mean difference = 1.17; 95% CI = 1.06, 1.28) had highest trust in governments/politicians and news media, respectively. However, there was little difference in trust in health, government, or news media between individuals using higher or lower levels of social media information sources. Conclusions: Trust is a key determinant of health, but has been politically fragile during this infodemic. High compliance with public health measures is key to combatting infectious diseases. In terms of people's trust, our findings suggest that politicians and governments worldwide should coordinate their response with health experts and authorities to maximise the success of public health measures.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".