Coercive control in a national U.S. self-report survey: Prediction of repeated intimate partner violence.
Bibliographic record
Abstract
Coercive, controlling behavior toward intimate partners correlates with physical intimate partner violence (IPV). We examined whether it also predicts subsequent IPV or other aggression. We conducted a secondary analysis of self-reports by 1,039 women and 509 men who participated in the first two waves of the Interpersonal Conflict and Resolution Study (Mumford et al., 2019). We defined coercive control as any reported perpetration at Wave 1 of threat to physically harm, threat to use information to control, or put down or disrespect their partner. The participants also reported perpetration of verbal abuse and physical or sexual aggression against intimate partners. We tested correlations of these behaviors with similar acts toward nonintimates (friends or unfamiliar persons) in Wave 1 and the prediction of physical violence in Wave 2, approximately 5 months later. Coercive control (14% of men, 26% of women) was correlated with physical or sexual IPV (8% of men, 15% of women) in both women and men and with physical violence and coercive control to nonintimates. In logistic regressions entering Wave 1 physical IPV on the first step, Wave 1 coercive control was a significant independent predictor of Wave 2 physical IPV overall, and for men but not women. Coercive control did not independently predict nonintimate physical violence. Coercive control toward an intimate partner is a unique predictor of physical IPV among men. Future research should use improved measures of coercive control and further examine coercive control as an indicator of general antisociality. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".