Sociodemographic Factors and Beliefs About Medicines in the Uptake of Pharmacogenomic Testing in Older Adults
Bibliographic record
Abstract
Aim: To assess the impact of sociodemographic factors and beliefs about medicines on the uptake of pharmacogenomic testing in older adults in a public healthcare system. Materials & methods: Data are based on a sample of 347 primary care older adults. Results: Most respondents (90%) were willing to provide a saliva sample and 47% were willing to pay for it. Increased age (odds ratio: 0.91; p = 0.04) and negative beliefs about the harmfulness of medicines (odds ratio: 0.68; p = 0.02) were associated with a decreased willingness to provide a sample. Lower education (less than university, odds ratio: 0.54; p = 0.04) was associated with a decreased willingness to pay. Conclusion: Education and beliefs about medicines are important factors in the acceptability of pharmacogenomic testing in older adults.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".