Genetic Information Access, a Legal Perspective: A Duty to Know or a Right Not to Know, and a Duty or Option to Warn?
Bibliographic record
Abstract
Abstract It is often argued that concern with basic personal privacy suggests that individuals should be allowed to decide in isolation whether to obtain genetic information through testing and whether to make the results available to others. However, the familial nature of genetic information complicates matters by raising question regarding whether an individual has a moral duty to discover and/or reveal existing genetic information to possibly affected family members. Alternatively, if this information would harm family members, do individuals have a duty not to obtain, or at least not to divulge, genetic test results? What role, if any, should legislation play in dealing with access to genetic information at the familial level? Do health care professionals have a responsibility or a right to inform close family members of the genetic status of a related individual? We consider the personal, familial and public health care perspectives regarding this debate. Key Concepts Many individuals face conflicting psychological effects regarding the anticipation of obtaining genetic test results. Despite the potential usefulness of genetic information, the majority of individuals facing potentially devastating genetic diseases opt not to undergo testing. Results of an individual's genetic tests are often of interest to family members which raises complications regarding individual privacy. Controversy also surrounds the responsibility of health care professionals whether to maintain patient privacy or inform affected family members. Courts and lawmakers struggle with the question whether individual privacy should be respected when genetic information may be of significant interest to family members. Courts have recognized that in some circumstances, health care providers have an option to warn family members of a genetic risk, when there is a significant risk of a serious and avoidable harm. A strong case can be made that, with few exceptions (e.g. PKU), genetic information should fall under the usual rules protecting individual privacy and confidentiality. Future discoveries in genetic information may lead to more exceptions to the duty of confidentiality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.090 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.032 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".