Minority Women Victims of Recent Sexual Violence: Disparities in Incident History
Bibliographic record
Abstract
AIMS: The aims of this descriptive study were to (1) describe assault and care characteristics and (2) determine differences in assault and care characteristics between black women and white women who sought emergency care following sexual violence. METHODS: A retrospective, cross-sectional design was used to examine forensic examination data characterizing the incident history reported by women victims of sexual violence at the time of a forensic nurse examination. Examinations occurred at an urban university-affiliated hospital emergency department (ED) over a 2-year period. RESULTS: Of the women (n = 173) seeking care in the ED, 58.4% were black and 41.6% were white. When compared with white women, black women were more likely to have weapons used in their assaults (42.6% vs. 16.7%, p < 0.00) and to be assaulted in the city rather than the suburbs (82.8% vs. 56.5%, p < 0.00). In general, substance use prior to the assault was reported to have occurred in 49.1% of the victims and 41% of the assailants; however, differences existed in the type and pattern of substance use by race/ethnicity. Black victims were more likely to report use of illicit drugs (28.7% vs. 12.5%, p = 0.01). White women were more likely than black women to report personal alcohol use prior to their assault, with significant differences for drinking by victims (47.2% vs. 23.8%, p = 0.01) or assailant use of alcohol (47.2% vs. 23.8%, p = 0.00). White women were more likely than black women to report both they and the assailant had used some type of substance (38.9 vs. 21.8, p = 0.01). Black women were more likely to arrive to the ED via EMS services (45.5% vs. 29.2%, p = 0.03). There were no reported differences in care characteristics by race. CONCLUSIONS: Findings from this study suggest that differences exist in assault characteristics between black and white women. Use of substances, including alcohol, plays an important role in sexual violence in women and should be a focus of preventive intervention initiatives when conducting a forensic examination. Both coordinated responses and comprehensive, individualized care by specially trained providers are important in the emergency care of minority women who are victims of recent sexual 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".