Prediction of Repeat Visits by Victims of Intimate Partner Violence to a Level III Trauma Centre
Bibliographic record
Abstract
Background. The purpose of this study was to describe and contrast the population of persons presenting to a Vancouver hospital emergency department two or more times with those presenting once.Methods. Subjects for this study had disclosed intimate partner violence on at least one visit to Vancouver General Hospital Emergency Department during the study period 1997–2009. We compared sociodemographic characteristics, presenting complaints and disposition on discharge among single versus repeat visitors.Results. We identified 2246 single visitors and 257 repeat visitors. In a multivariate model, repeat visitors to the ER were more likely to be of First Nations (aboriginal) status, odds ratio (OR) 2.29, 95% confidence intervals (1.30–4.01); to have had a history of previous abuse 3.38 (1.88–6.08); to have received threats of homicide 2.98 (1.74–5.08); and to present with mental illness 3.03 (1.59–5.77). Police involvement was protective against repeat visits 0.54 (0.36–0.98).Conclusion. Persons with potential for multiple visits to the emergency room can be characterized by a number of factors, the presence of which should trigger targeted assessment for violence exposure in settings where assessment is not routine.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".