How We Talk About “Perpetration of Intimate Partner Violence” Matters
Bibliographic record
Abstract
Many partners and children who are affected by intimate partner violence (IPV) are unable to leave abusive situations that put their health and safety at risk. Family physicians provide care for people who perpetrate IPV and are in a role that may allow them to recognize and counsel patients who are using violence. Appropriate referrals can potentially help these patients access effective interventions such as certified battering intervention programs in a manner that prevents violence for their families. The language used by physicians can facilitate or impede disclosures among patients perpetrating IPV who may be open or willing to discuss their use of violence. Talking about their behavior in ways that patients perceive as derogatory or confrontational may alienate people who use violence from initiating or engaging in meaningful discussions about their abusive behaviors in clinical settings and getting the help they need to stop their violence. To enable patients to safely talk about their own perpetration of violence, physicians need to develop appropriate language and a nuanced, evidence-based approach to broaching and discussing this issue with patients. As with other patient populations, being labelled may not accurately describe their identity, behavior, nor experiences, and result in them avoiding care. In keeping with trauma-informed approaches, we provide possible examples of respectful nonjudgmental language and nonthreatening clinically appropriate questions for people who use violence. Additional research is needed to identify how best to discuss perpetration of IPV with patients to help initiate change in their behavior while maintaining victim safety.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".