A Comparison of Risk Factors for Intimate Partner Violence–Related Injury Across Two National Surveys on Violence Against Women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study compares risk factors for intimate partner violence–related injury across two national data sources on violence against women, the Canadian Violence Against Women Survey and the National Violence Against Women Survey in the United States. After equating the data sets as much as possible on the types of violence experienced and risk factors, the authors determined which risk factors in each data source predicted injury and compared the magnitudes of associations between risk factors and injury across the data sets. The article presents results on bivariate and multivariate findings, model fit across the data sets, and statistical comparisons of findings across the data sets. Obtaining convergent findings across data sources on risk factors for injury will allow public health practitioners to intervene more effectively with women at risk for experiencing violence-related injuries perpetrated by spouses.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it