Duty to warn for genetic testing: the importance of understanding harm when practically applying The president's commission standards of disclosure
Bibliographic record
Abstract
The field of genetics is unique as test results reveal information about multiple individuals. When a hereditary condition is identified, healthcare professionals face an ethical dilemma between their duty of confidentiality toward their patients and their moral obligation to warn relatives of possible harm. The standards developed by The 1983 President's Commission1 guide healthcare professionals with this decision-making process. This thesis will argue that these standards of disclosure are defensible in the face of common criticisms of genetic information disclosure and are a good guide for health care professionals challenged with this ethical dilemma. However, there are challenges when practically applying these standards because of the standards’ reliance on the notion of harm. Harm is complex, and when using the standards, we must distinguish between a harmful action and a wrongful action. This thesis will argue that this distinction must be made when practically applying these standards to avoid a mistake in our understanding of the situation. An improper understanding would result in a moral dilemma as the rights of another would be infringed upon in an unjustified manner.
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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 | 0.182 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.033 | 0.040 |
| Insufficient payload (model declined to judge) | 0.002 | 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".