The Assessment of Legal Knowledge among Obstetricians and Gynecologists about Legal Consequences of Assisted Reproductive Techniques
Bibliographic record
Abstract
Background and Aim : With the emergence and proliferation of assisted reproductive techniques, the needs of societies have altered. Therefore, measuring the level of legal awareness of gynecologists and obstetricians as the primary consultants of infertile couples can be significantly influential. In this study, we aimed to provide insight into the lack of legal knowledge of this group of specialists, which undermines the quality of healthcare services. Materials and Methods : This cross-sectional study was conducted among 80 gynecologists and obstetricians in Tehran, Iran, during 2016. We used a 26-item questionnaire on the common legal challenges of infertile couples. Ethical Considerations : Verbal informed consent of the participants was obtained after explaining the purpose of the study and the positive consequences of enhancing medical education and improving doctor-patient relationship. Findings : In general, 30% of the participants were male and 70% were female (age range: 35-75 years). Further, 24% of the participants did not respond to the questionnaire due to limited or lack of knowledge, and 28% knew the permitted types of artificial insemination by Iran’s laws. Concerning the basic rights of the child, 17% provided the correct response, and regarding the parental rights, 4% were aware of the existing legal condition. Finally, on the subject of surrogacy contracts, 22% were cognizant of the critical basics. Conclusion : Based on the mentioned results and due to the deep gap between the fields of law and medicine, improvement of the existing curriculum in Iran is highly recommended. Citation: Mohseni T, Chaichian Sh, Mohseni M, Moazzami B. The Assessment of Legal Knowledge among Obstetricians and Gynaecologists about Legal Consequences of Assisted Reproductive Techniques. Bioeth Health Law J. 2017; 1(3):27-30.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".