Asking the Right Questions: Screening Men for Partner Violence
Bibliographic record
Abstract
With lifetime intimate partner violence (IPV) victimization rates for self-identified men between 14% and 20%, and an expanding understanding of gender as a nonbinary construct, practitioners in some clinical environments have expressed interest in screening all patients for IPV. Yet, few IPV screening instruments have been validated for use in nonfemale populations. This research tests the appropriateness and acceptability of a screening instrument developed for use with women.A literature review was completed to determine the current state of research into IPV screening practices tailored to men. Next, cognitive interviews were conducted to test a 9-question IPV screening instrument with men considered at average and elevated risk for experiencing partner violence. Participants were read the questions aloud and asked about item comprehension and question appropriateness and acceptability.The literature review uncovered no published reports describing routine clinic based IPV screening of men, and only two screening instruments had been validated with men. Twenty men participated in cognitive interviews from a variety of settings in a large urban center. All participants accurately described the intended meaning of each question and verified the appropriateness of asking the questions.This work addresses the gap in research on routine IPV screening with men, building on efforts to screen individuals and support improved health and response to violence to those across the gender spectrum.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".