Consumer-perceived risks and choices about pharmaceuticals in the environment: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: There is increasing concern that pollution from pharmaceuticals used in human medicine and agriculture can be a threat to the environment. Little is known, however, if people are aware that pharmaceuticals may have a detrimental influence on the environment. The present study examines people's risk perception and choices in regard to environmental risks of pharmaceuticals used in human medicine and for agricultural purposes. METHODS: A representative sample of the U.S. population (N = 640) was surveyed. Respondents completed a hypothetical choice task that involved tradeoffs between human and environmental health. In addition, it was examined how much people would support an environment policy related to drug regulation. RESULTS: For agricultural pharmaceuticals, respondents reported a high level of satisfaction for a policy requiring farms to limit their use of antibiotics. In the domain of pharmaceuticals used in human medicine, we found that people were willing to consider environmental consequences when choosing a drug, but only when choices were made about treatment options for a rather harmless disease. In contrast, when decisions were made about treatment options for a severe disease, the drug's effectiveness was the most important criterion. CONCLUSIONS: It can be concluded that the environmental impact of a drug will be hardly considered in decisions about pharmaceuticals for severe diseases like cancer, and this may be due to the fact that these decisions are predominantly affective in nature. However, for less severe health risks, people are willing to balance health and environmental considerations.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".