The Emergency Department and Victims of Sexual Violence: An Assessment of Preparedness to Help
Bibliographic record
Abstract
The Emergency Department (ED) is a key source of care for victims of sexual violence but there is little information available about the extent to which EDs are prepared to provide this care. This study examines the structural and process factors that the ED has in place to assist victims. A survey of all 82 publicly accessible EDs in the Commonwealth of Virginia was conducted (RR 76%). In general, the EDs provide the recommended medical care to victims. However, at least half do not have the needed resources in place to effectively assist victims and most (80%) do not provide regular training to their medical staff about sexual violence. Further, almost one-quarter do not have a relationship with a local rape crisis center. It is recommended that each ED partner with local rape crisis centers to provide training to their staff and to ensure continuity of support for victims. It is also suggested that the state government explore ways in which a forensic (SANE) nurse be made available to every victim of sexual violence that presents to the ED for medical assistance. Ideally, each ED would become part of a community-wide Sexual Assault Response Team (SART) in order to provide comprehensive care to victims and thorough evidence collection and information to law enforcement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".