Who Is Most at Risk for Intimate Partner Violence?
Bibliographic record
Abstract
Whole population studies on intimate partner violence (IPV) have given contradictory information about prevalence and risk factors, especially concerning gender. The authors examined the 1999 Canadian General Social Survey data for gender patterns of physical, sexual, emotional, or financial IPV from a current or ex-partner. More women (8.6%) than men (7.0%, p = .001) reported partner physical abuse in general, physical IPV causing physical injury (p < .0001), sexual abuse (1.7% vs. 0.2%, p < .0001), and financial abuse (4.1% vs. 1.6%, p < .0001). There were no gender differences for partner emotional abuse. Significant risk factors after multivariate modeling for physical/sexual IPV were younger age, being divorced/separated or single, having children in the household, and poor self-rated physical health. These findings from a large, randomly generated data set further refine our understanding of the risk profile for IPV in the developed world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".