A discrete choice experiment of preferences for genetic counselling among Jewish women seeking cancer genetics services
Bibliographic record
Abstract
To determine which aspects of breast cancer genetic counselling are important to Ashkenazi Jewish women, a discrete choice experiment was conducted. Participants consisted of 339 Australian Ashkenazi Jewish women who provided a blood sample for research used to test for Ashkenazi Jewish ancestral mutations in the genes BRCA1 and BRCA2, and were offered their genetic test result through a cancer genetics service. Main outcome measures were women's preferences for, and trade-offs between, the genetic counselling aspects of providing cancer, gene, and risk information (information); giving advice about cancer surveillance (surveillance); preparing for genetic testing (preparation); and, assistance with decision-making (direction). Respondents most valued information, about twice as much as advice about surveillance, four times as much as preparation for testing, and nine times as much as assistance with decision-making, which was least valued. Women's preferences were consistent with the major goals of genetic counselling, which include providing information and surveillance advice, and avoiding direction by facilitating autonomous decision-making. There were differences between the women in which aspects they most favoured, suggesting that counselling that elicits and responds to clients' preferences is more likely to meet clients' needs.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".