A Dyadic Longitudinal Study of Child Maltreatment and Sexual Well-Being in Adult Couples: The Buffering Effect of a Satisfying Relationship
Bibliographic record
Abstract
This study examined the contribution of child maltreatment (CM) to trajectories of couples' sexual well-being, and whether relationship satisfaction moderates these associations. Using a sample of 269 mixed-sex couples followed over one year, dyadic latent growth curve models showed both actor and partner effects. In terms of actor effects, women's emotional neglect was associated with lower initial levels of sexual satisfaction, and most types of women's CM were related to a sharper decrease over time in sexual satisfaction. Men and women's emotional abuse and neglect, and women's sexual abuse, were associated with lower initial levels of sexual function. Men and women's emotional neglect and women's emotional abuse were related to higher initial levels of sexual distress. Women's sexual abuse was associated with a steeper increase in sexual distress. In terms of partner effects, women's emotional neglect was associated with lower initial levels of partner sexual satisfaction, and women's emotional abuse and neglect, with lower initial levels of partner sexual function. Greater relationship satisfaction buffered some of these negative effects. Given that sexual well-being requires a context in which the individual feels safe, all forms of CM may affect sexual well-being, although a satisfying relationship may buffer some of these effects.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".