Disclosing Genetic Information to Family Members: The Role of Empirical Ethics
Bibliographic record
Abstract
Abstract The familial and predictive nature of genetic information raises ethical issues regarding its disclosure to biological relatives. Arguments in the bioethics literature have centered on the right of the patient to privacy and confidentiality versus the right of family members to receive information that is clinically relevant to them. Empirical research has shown that although the need for disclosure within the family is rarely contested by patients, they are preoccupied by the ethical dimensions of their ‘genetic responsibility’, share a desire to protect relatives from the possible adverse effects of disclosure, and need guidance regarding what, why, to whom, when and how genetic information should be disclosed to mitigate adverse outcomes. Empirical ethics thus contributes to important insights and help reframe the debate to better address the lived experiences of patients, family members and healthcare professionals. Key Concepts: Disclosure of genetic information within the family raises particular ethical issues. The disclosure debate has been framed as a conflict between respect for autonomy (i.e. the duty of the clinician to respect patient confidentiality) and beneficence/‘do no harm’ (i.e. the clinician's duty to warn others). It is suggested that a patient's relatives have a right to know genetic information when it is clinically relevant to them. Most patients feel a genetic responsibility to inform family members in order to promote their health and well being. Empirical studies have shown that disclosure decisions are complex and influenced by numerous factors. Empirical ethics involves empirical studies which provide information that can frame ethical debates, enhance normative analysis and inform clinical practices.
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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 | 0.152 | 0.245 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.077 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".