State Intervention in Couples’ Reproductive Decisions: Socioethical Reflections Based on the Practice of Preimplantation Genetic Diagnosis in France
Bibliographic record
Abstract
Adopting socioethical and anthropological perspectives, this article addresses the impact of state intervention in the reproductive life of couples who consult for preimplantation genetic diagnosis (PGD) in France. Our main objective is to identify and analyze the socioethical problems flowing from French legislation as related to PGD and from its implementation. Methods included review and analysis of the relevant literature, ethnographic research in the three centers accredited to perform PGD, and participant observation (990 hours), with 79 semistructured interviews. Ethical problems identified were: (1) discrimination based on sexual orientation and the requirement for adherence to a traditional model of the couple and the family; (2) inequities in access to PGD; (3) restrictions on couples’ autonomy; and (4) breaches of respect for private life. We conclude that the state could improve the ethical conditions in which PGD is practiced by: (1) establishing educational programs in ethics to support members of multidisciplinary centers for prenatal diagnosis; (2) conducting empirical studies on the social acceptability of PGD; and (3) conducting empirical studies on the extent of state intervention in the reproductive life of couples likely to have recourse to reprogenetic services.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| 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".