Best-worst scaling and ranking exercise to understand decision-making and preferences for genetic testing in moderate-risk individuals
Bibliographic record
Abstract
This sub-study is part of the larger project entitled ‘PreventGene (Precision Medicine Expansion to Moderate Risk Patients): Shaping the Future of Adult Precision Health Genomics’, funded by the Calgary Health Foundation in Canada. PreventGene is a joint research project between the University of Calgary and Alberta Health Services, which aims to establish an integrated clinical and academic Adult Precision Health Genomics Program for moderate-risk individuals at the Richmond Road Diagnostic and Treatment Centre. In this sub-study, we plan to quantitatively estimate the relative importance of factors that influenced individuals' decision-making for or against primary genetic testing (breast cancer or aortic disease) and/or secondary analyses (secondary findings unrelated to breast cancer or aortic disease). Surveys will be electronically distributed to eligible participants after they have consented to or declined genetic testing. The survey is expected to elicit preferences around genetic testing by using a best-worst scaling (BWS) for primary genetic testing and a ranking exercise for secondary analyses. The study aims are: 1. To elicit preferences of individuals at moderate risk for different testing cohorts (breast cancer, aortic disease and non-tester) and testing types (primary genetic testing and secondary analyses) to have or not have genetic testing; 2. To inform what are the most and least important factors that influenced their decision-making regarding primary genetic testing and/or secondary analyses; and 3. To identify and understand any heterogeneity in preferences for genetic testing based on demographic characteristics and attitudes, as well as experiences towards genetic testing in general. The data collection started in March 2025 and is still ongoing. It is expected that the data collection will be finished by summer 2026.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".