Willingness to take drugs to prevent serious chronic diseases (自愿服用药物以预防严重的慢性疾病)
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine an individual's willingness to take a preventive therapy for each of two chronic diseases (type 2 diabetes [T2D] and dementia) when provided with varying likelihoods of acquiring the disease. METHODS: After reading a description of two chronic diseases (i.e. T2D and dementia), 345 student volunteers at McMaster University rated their likelihood of taking a drug that could halve their chance of developing each disease, assuming a 1-year risk of developing the disease of 50%, 25%, and 10%. A five-point Likert scale was used to collect responses. RESULTS: Assuming an annual incidence of 50%, 27% of respondents were neither likely nor very likely to take a therapy that halved the annual incidence of T2D and 13% were neither likely nor very likely to take a therapy that halved the annual incidence of dementia. Higher quoted incidence rates of the disease and a personal history of a chronic illness significantly increased willingness to take such therapy. CONCLUSIONS: A high proportion of young educated adults have ambivalent or negative attitudes regarding the use of pharmacological therapy to prevent serious health outcomes even when the absolute 1-year risk of these outcomes is very high.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".