A Demographic Profile of Mothers and Their Children Who Are Victims of Family and Domestic Violence: Using Linked Police and Hospital Admissions Data
Bibliographic record
Abstract
The aim of this study was to examine the key sociodemographic characteristics of Australian mothers and their children who were victims of family and domestic violence (FDV) that resulted in the male perpetrator being criminally charged for the offense or the mother being hospitalized. A population-based retrospective cohort study using de-identified linked health and police data of mothers with children born 1987–2010 who were victims of FDV 2004–2008 was utilized. Results indicate that mothers who were identified in police data are different demographically from those identified in health data and differed again from mothers identified in both health and police data. Within Western Australia, 3% of the population identify as Aboriginal; however, 44% of mothers identified as victims in police data and 73% within the health data were Aboriginal. Of the mothers identified in police data, 30% were under 25 years of age at their first assault recorded in police data compared with 21% in those identified in both police and hospital data. Most mothers identified as victims of FDV in police data had children present at their assault (60.6%). Prevalence of FDV exposure, identified in police data, was significantly different in Aboriginal children compared with non-Aboriginal children. Aboriginal children had a 19-fold ( p < .0001) increased difference in prevalence of exposure compared with their non-Aboriginal counterparts. The study reveals the challenges in identifying victims of FDV when relying on a single data source for research and highlights the need for multiple datasets when investigating FDV. The overrepresentation of Aboriginal mothers and children should be taken in the context of the long-lasting impact of colonization. As such, prevention and early intervention strategies need to be underpinned by Aboriginal communities’ cultural authority.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".