Family stories and the use of heuristics: women from suspected hereditary breast and ovarian cancer (HBOC) families
Bibliographic record
Abstract
The practice of medicine will increasingly be medicine of the family rather than the traditional physician/patient dyad, especially where a genetic condition is involved. This study explores how clients from suspected hereditary breast and ovarian cancer (HBOC) families seeking cancer genetics risk counselling are influenced by family stories and the use of heuristics (inferential shortcuts used to make sense of complicated information) in interpreting and applying genetic information they receive, and suggests ways in which genetic counsellors can integrate family context into their traditional counselling practices. We conducted an exploratory, qualitative study at a major clinical and research cancer centre in the United Kingdom from January to June 2000 which was reviewed by the hospital clinical research and ethics committees. Twenty-one semi-structured, in-depth interviews were conducted using a purposive sample of women coming to the cancer genetics clinic for the first time, supplemented by five months of clinical observation at weekly clinics. In addition to many family stories based on the number and outcomes of the cancers in their families, we noted: (1) fragments of stories, (2) secret stories, (3) emerging explanations and (4) misconceptions, We did not find widespread intergenerational family myths, The women used three main heuristics in interpreting their breast/ ovarian cancer risk: (1) representativeness, (2) availability and (3) illusion of control, as well as what Kahneman refers to as the Peak and End rule. Recent psychological research indicates that illusions of control may have positive affects on both physical and mental health. This may pose a future ethical issue for genetic counsellors in determining how to balance the benefit of positive illusions with the delivery of statistical probabilities of risk.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".