Predictors of Individual and Interpersonal Adjustment Among Non-offending Partners of Individuals With Histories of Sexual Offenses
Bibliographic record
Abstract
Recent research indicates that the consequences of sexual offenses extend beyond target victims, including to non-offending partners of individuals with sexual offense histories. However, little research has focused on non-offending partners’ wellbeing and relationships with persons with sexual offense histories leading up to and following acts of sexual aggression. Non-offending partners may be secondary victims of their partners’ offenses in managing psychological difficulties (e.g., guilt, shame), social stigma and isolation, fear for their safety, or difficulties in their romantic relationships resulting from their partners’ sexual offenses, often with minimal supports. The current study examined key correlates of individual and interpersonal adjustment among 207 non-offending partners of individuals with histories of sexual offenses who were residing in Canada (n = 36) or the United States (n = 171). Findings indicate that positive changes due to the offense (i.e., improved finances), self-esteem, interpersonal adjustment, instrumental support, lower levels of acceptance, and humor positively predicted individual adjustment. Interpersonal adjustment was predicted by trust, intimacy, partner’s stress communication, and problem-focused and emotion-focused common dyadic coping. Findings highlight the need for services for non-offending partners, including interventions that address self-esteem and practical difficulties resulting from the offense, and couples therapy to address trust issues, intimacy concerns, and shared coping with stressors related to the offense.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".