Willingness to Pay for Genetic Testing: A Study of Attitudes in a Canadian Population
Bibliographic record
Abstract
BACKGROUND: This article reports results of a 2008 telephone survey of approximately 1,200 residents of the Province of Alberta, Canada. The majority of respondents reside in urban centers, have some post-secondary education, and report annual family income near or above the Canadian average. The goal was to explore attitudes and interest regarding different types of genetic tests. METHODS: Respondents were asked about their willingness to pay for tests to gain information about genetic factors related to manageable conditions, serious, unpreventable disease, healthy food choices, psychiatric conditions, going bald (asked of men only), and gaining weight. The price categories were CAD 0, CAD 1-499, CAD 500-1,999 and CAD 2,000+. Respondents were also asked about factors that would motivate interest in genetic testing, such as availability of treatment, curiosity, and reproductive decision-making. They were also asked if the public health insurance system should pay for certain types of tests. RESULTS: Across all test categories, few respondents expressed willingness to pay more than CAD 500 out of their own pocket. 62% stated that the public health insurance system should pay for genetic tests for manageable conditions and opinion was divided about whether the government should fund tests for serious, unpreventable conditions and tests to inform healthy eating choices. CONCLUSION: The principal motivator for interest in genetic testing was to learn clinically relevant details to inform health-related decisions. Curiosity about genetic risk had only a modest impact on consumer interest. In general, younger respondents (18-35 years) expressed somewhat greater willingness to pay than older respondents, especially those 65 and older.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".