Public trust and genomic medicine in Canada and the UK
Bibliographic record
Abstract
Background: Genomic medicine could improve precise risk stratification, early prevention, and personalised treatment across a broad spectrum of disease. As this reality approaches, questions on the importance of public trust arise. The success of genomic medicine initiatives is influenced by the public’s trust and willingness to engage. Specific social actors influential in the public's trust have been identified by the “Your DNA, Your Say” study, including doctors, researchers, and governments. This paper aims to identify and examine which specific social actors, if any, in Canada and the United Kingdom (UK) are the most trustworthy and influential to engage the public in genomic medicine. Methods: Using data from the ‘Your DNA, Your Say’ study, logistic regression models and Pearson’s chi-square tests were conducted to explore trust in social actors across Canada and the UK. Results: The results demonstrate Canada and the UK significantly differ in public trust and willingness to donate. Non-profit researchers, domestic doctors, and personal doctors were identified to be the most influential and trustworthy social actors in Canada and the UK. Conclusions: The comparative results indicate that both countries would benefit from engaging the public through doctors and non-profit researchers. The UK could additionally support public trust by engaging with the public through the National Health Service. However, the results suggest that whilst public trust is significant, it may be neither necessary nor sufficient in influencing willingness to donate. Future research could do well to investigate how the importance of public trust compares in countries with lower public trust.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".