Measuring the Burden of Intimate Partner Violence by Sex and Sexual Identity: Results From a Random Sample in Toronto, Canada
Bibliographic record
Abstract
Debates on how sex, gender, and sexual identity relate to intimate partner violence (IPV) are longstanding. Yet the role that measurement plays in how we understand the distribution of IPV has been understudied. We investigated whether people respond differently to IPV items by sex and sexual identity and the implications this has for understanding differences in IPV burdens. Our sample was 2,412 randomly selected residents of Toronto, Canada, from the Neighborhood Effects on Health and Well-being (NEHW) study. IPV was measured using short forms of the Physical and Nonphysical Partner Abuse Scales (20 items). We evaluated the psychometric properties of this measure by sex and sexual identity. We examined whether experiences of IPV differed by sex and sexual identity (accounting for age and neighborhood clustering) and the impacts of accounting for latent structure and measurement variance. We identified differential item functioning by sex for six items, mostly related to nonphysical IPV (e.g., partner jealousy). Males had higher probabilities of reporting five of the six items compared to females with the same latent IPV scores. Being female and identifying as lesbian, gay, or bisexual were positively associated with experiencing IPV. However, the association between female sex and IPV was underestimated when response bias was not accounted for and outcomes were dichotomized as "any IPV." Common practices of assuming measurement invariance and dichotomizing IPV can underestimate the association between sex or gender and IPV. Researchers should continue to attend to gender-based and intersectional differences in IPV but test for measurement invariance prior to comparing groups and analyze scale (as opposed to binary) measures to account for chronicity or intensity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".