Between Harm and Desire: A Cross-Lagged Study of Psychological Intimate Partner Violence, Coercive and Controlling Behaviors and Sexual Satisfaction
Bibliographic record
Abstract
Psychological intimate partner violence (PIPV) and coercive and controlling behaviors (CCB) are highly prevalent in the general population. Considering couples' sexual and relational wellbeing are closely intertwined, it is surprising that no study has examined the temporal associations between PIPV and CCB, and couples' sexual satisfaction. This study examined the dyadic associations between PIPV, CCB and sexual satisfaction over time, and explored gender differences. A community sample of 406 mixed-sex couples completed measures of PIPV, CCB and sexual satisfaction at two time-points, one year apart. An autoregressive cross-lagged model following the actor-partner interdependence framework was tested. Results revealed small actor effects, indicating that higher PIPV perpetration in women and higher CCB perpetration in men at T1 were related to their own higher sexual satisfaction at T2. A medium partner effect showed that men's PIPV perpetration at T1 was linked to lower sexual satisfaction in their partner at T2. Large autoregressive effects indicated that PIPV, CCB, and sexual satisfaction were stable over time. Small partner effects revealed that higher sexual satisfaction in participants at T1 was related to higher sexual satisfaction in their partner at T2. Findings underscore the complex associations linking PIPV, CCB, and sexual satisfaction in couples.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".