The Nature and Extent of Recurring Intimate Partner Violence Against Women in the United States
Bibliographic record
Abstract
Physicians play a key part in society’s response to violence against women. Their professional role affords them the opportunity to talk privately with women, identify victims of abuse, and offer support. However physicians’ own history of victimization may undermine their ability to assist battered women. We used an anonymous, selfreport survey to describe the violence history of students enrolled at a medical school, and explore the relationship between students’ violence history, current well being, help seeking, and expected future impact on education and clinical care of patients. Valid surveys were returned by 472 of 810 students. 53% reported experiencing one or more forms of severe violence (30% reported severe child physical abuse; 6% child sexual abuse by a family member; 13% child sexual abuse by a non-family member; 22% severe partner violence; 7% adult sexual assault). Participants with a history of severe violence were more likely to report feeling downhearted and blue. Some participants with a severe violence history reported that these experiences would interfere with their ability to feel good about themselves (32%), develop relationships (38%), work effectively (11%), participate in courses dealing with violence and abuse (15%), and assist patients with experiences similar to their own (18%). Women students experienced more severe physical and sexual violence, and expected more future difficulties in their personal and work life. Results are discussed in the context of the history of gender discrimination in medicine, and the need for new methods for training physicians to identify and assist victims of partner violence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".