Connaissances de l’anatomie génitale féminine et compréhension des constatations médico-légales dans les situations de violences sexuelles chez les acteurs de la chaîne pénale
Bibliographic record
Abstract
Introduction: The forensic physician plays a crucial role in the judicial process of victims of sexual violence, particularly by documenting any observed injuries. The absence of lesions is still frequently, and wrongly, perceived as the absence of violence. This study aimed to assess how criminal justice actors understand forensic findings and to examine the relationship between this understanding and their knowledge of female genital anatomy. Materials and Methods: Anonymous surveys were conducted among magistrates and judicial police officers (female genital anatomy and understanding of findings); police officers from Paris police stations (understanding of findings); and attendees at a public science event (female genital anatomy). Results: Responses were obtained from 104 magistrates and judicial police officers, 76 police officers outside the judicial police, and 88 public attendees. The results revealed significant gaps in knowledge of female genital anatomy, similar between justice actors and the general public, particularly concerning the location of the hymen and the vagina. While forensic findings were generally well interpreted, some misconceptions persisted, notably regarding the hymen, still perceived as a “barrier” proving the absence of vaginal penetration in adult women. The absence of genital lesions was also often equated with the absence of sexual violence, and more than a quarter of respondents believed that a forensic physician could attest to a woman’s virginity. Among magistrates and judicial police officers, better anatomical knowledge was statistically associated with better understanding of forensic findings. Discussion and Conclusion: Our study highlights the persistence of misunderstandings in the interpretation of certain forensic findings in cases of sexual violence. These misconceptions are linked to stereotypes perpetuated for centuries by forensic medicine and were less frequent among respondents with stronger knowledge of female genital anatomy. This work underscores the importace of providing high-quality forensic training to criminal justice actors and, for forensic physicians, of explicitly clarifying their findings in forensic reports.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".