The Naturalness Bias Influences Drug and Vaccine Decisions across Cultures
Bibliographic record
Abstract
Past research with North American participants has demonstrated a naturalness bias in the medical context: people prefer natural drugs to synthetic drugs under a variety of situations. Does such a bias exist in other countries (such as China) where cultural values and practices are quite different from those in the United States? We conducted 3 studies ( N = 1,927) to investigate the naturalness bias with drugs and vaccines across cultures with American, Canadian, and Chinese participants. In studies 1A and 1B, participants chose or rated drugs (natural v. synthetic) for a hypothetical medical issue. The drugs were presented as having identical effectiveness and side effect profiles. Study 2 focused on a different medical context, vaccines, and required participants to rate their likelihood of taking vaccines (made from either more natural or more synthetic ingredients) for a harmful virus. The naturalness bias occurred across cultures in studies 1A and 1B, although it was not significant among Chinese participants in study 1B. In study 2, Chinese participants showed a stronger naturalness bias than Americans did, and safety concerns mediated the effect. Perceived safety accounted for the naturalness bias among Americans and Canadians, but did so only among Chinese in study 2. Overall, the results suggest that the naturalness bias in drug and vaccine decision making occurs across cultures, but Chinese participants may be more sensitive to the medical context. Highlights The naturalness bias — preferring natural to synthetic drugs or vaccines — occurred across cultures (Americans, Canadians, and Chinese). Chinese participants showed a stronger naturalness bias than Americans did when the medical context was focused on vaccination, and safety concerns mediated this effect. The naturalness bias may influence medical decision making across cultures, but Chinese participants may be more sensitive to naturalness in a vaccine context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".