Exploring the Impact of Criminal Harm on Personal and Relationship Wellbeing in Romantic Couples
Bibliographic record
Abstract
This pair of studies investigates the impact of criminal harm, including both direct and vicarious experiences, on the wellbeing of individuals and their romantic partners within close relationships.Study 1 examines the association between criminal harm and adverse mental and physical health outcomes, considering variations based on the nature of harm (direct vs. vicarious) and crime type.Sixty-eight romantic couples participated, reporting their experiences of criminal harm and rating their personal and relationship wellbeing.Results underscore the importance of dyadic dynamics in understanding outcomes within couples, highlighting the need to address vicarious trauma in intimate relationships.Study 2 builds on prior research by focusing specifically on the dyadic effects of sexual assault within romantic partnerships.Fortythree couples participated in a year-long investigation, engaging in lab-based conversations while physiological responses were recorded.Findings do not support hypotheses linking direct experiences of sexual assault to negative mental and physical health outcomes, though sample size was limited.Furthermore, inconsistencies were noted in the impact on partners vicariously exposed to sexual assault across different wellbeing indicators.This ongoing research contributes valuable insights into the impact of vicarious harms of sexual assault on personal and relationship wellbeing, offering implications for supporting affected 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".