Using couple-level data to examine the relation between social information-processing and intimate partner violence among men and women in dating relationships
Bibliographic record
Abstract
The aim of the current study was to use couple-level data to examine negative emotions and social information-processing (SIP) abilities as risk factors for intimate partner violence (IPV) among 100 dating couples (N = 200; mean age = 21.45 years). Crick and Dodge's (1994) SIP model was used as a guiding theoretical framework. Participants read a series of hypothetical conflict situation vignettes and responded to questionnaires to assess negative emotions and various facets of SIP including attributions for partner behaviour, generation of response alternatives, and response selection. The Revised Conflict Tactic Scales (CTS2; Straus, Hamby, Boney-McCoy, & Sugarman, 1996) were used to assess how often acts of physical aggression occurred in the preceding year. Bivariate correlations revealed negative emotions and SIP abilities were significantly intercorrelated. A series of negative binomial mixed-model regressions were conducted based on the actor-partner interdependence model (APIM; Kenny, Kashy, & Cook, 2006). Significant results emerged for the response generation and negative emotion models. Results suggested that participants who generated a lower number of coping response alternatives were at greater risk of victimization (actor effect). Women were at greater risk of victimization if they had partners who generated a lower number of coping response alternatives (sex by partner interaction effect). Generation of less competent coping response alternatives predicted greater risk of perpetration among men, whereas generation of more competent coping response alternatives predicted greater risk of victimization among women (sex by actor interaction effects). Finally, two significant actor by partner interaction effects emerged for the negative emotion models. Participants who reported similar levels of negative emotions as their partners were at lowest risk of perpetration, whereas participants who reported discrepant levels of negative emotions from their partners were at greatest risk of perpetration. Participants who reported low levels of negative emotions were at lowest risk of victimization regardless of their partner's emotions; however, participants who reported high levels of negative emotions were at greatest risk of victimization if they had partners who reported low levels of negative emotions. Results from the current study have implications for researchers and clinicians interested in addressing the problem of IPV.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".