The influence of question wording on assessments of interest in genetic testing for breast cancer risk
Bibliographic record
Abstract
The purpose of this study was to compare the results of different measures of interest in genetic testing for breast cancer risk. A telephone survey of a random sample of women without breast cancer was conducted in British Columbia, Canada. Interest in genetic testing for breast cancer risk was measured in three ways: (1) an unprompted assessment of interest, (2) assessment of interest when prompted with a hypothetical offer of testing, and (3) assessment of interest when provided with supplementary information. Substantial differences in reported levels of interest in genetic testing were observed across the different assessment approaches, with the unprompted assessment of interest resulting in lowest levels of interest. The highest levels of interest were observed when the assessment of interest was prompted with a hypothetical offer of testing. Factors predicting interest in genetic testing varied depending on the assessment measure used. These findings suggest that more attention must be given to measurement issues, including complete reporting of measures used in research, development of standardized approaches to assessing interest in genetic testing, and more rigorous psychometric evaluations of measures of interest in genetic testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".