Genetic Test Results and Disclosure to Family Members: Qualitative Interviews of Healthcare Professionals’ Perceptions of Ethical and Professional Issues in France
Bibliographic record
Abstract
The benefit of disclosing test results to next of kin is to improve prognosis and-in some cases-even prevent death though earlier monitoring or preventive therapies. Research on this subject has explored the question of intra-familial communication from the standpoint of patients and relatives but rarely, from the standpoint of healthcare professionals. The purpose of this study was to interview relevant healthcare professionals in France, where legislation framing the issue was recently passed. A qualitative study consisting of semi-structured interviews was set up to get a clearer picture of the challenges arising from this issue, its consequences in terms of medical care-service practices, and the positions that frontline professionals have taken in response to this new legal framework. The findings from eight interviews with 7 clinical geneticists and 1 genetic counselor highlight very different patterns of practices among care services and among the genetic diseases involved. It is equally crucial to investigate other issues such as the nature of genetic testing and its consequences in terms of disclosing results to kin, the question of the role of genetic counseling in the disclosure process, the question of prescription by non-geneticist clinicians, and practical questions linked to information content, consent and medical follow-up for patients and their relatives.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 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".