Medical care use among women before and after sexual assault: a population study
Bibliographic record
Abstract
We set out to compare medical care utilisation patterns between women who were sexually assaulted and women who were not sexually assaulted in the year before and the year after the assault. Cases studied were 690 females aged 15 and over who had undergone a forensic evidence examination for sexual assault between April 1996 and March 1997 and the same number of female age-matched controls. This was a population-based, matched case-control study using administrative databases of medical encounters (only physician services). Outcomes included physician visits by physician specialty; clinic and hospital diagnoses and hospital discharges. Of the cases, 38.4% were aged 1519; 21.4% aged 2024; and 40.2% aged 25 or older. Before and after the sexual assault, cases were more frequent users of medical services than controls. Cases were seen more often, received more medical services and were more likely to be hospitalised than controls in both time periods. Cases were also more likely to be diagnosed with a vast range of physical and psychological problems during the period of study. The persistence of mental and physical health problems before and after a forensic examination for sexual assault presents a challenge. Of paramount concern are issues concerning informed consent to undergo the examination. Collection and documentation of evidence may be important for victims with severe mental illness who may have more difficulty testifying in court. Our study suggests the need for future research examining the reasons why women do not undergo the examination both in terms of whether it is offered and declined or not offered to them.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".