Variable Sexual Satisfaction in Pregnancy: A Latent Profile Analysis of Pregnant Wives and Their Husbands
Bibliographic record
Abstract
Although not all couples achieve high levels of sexual satisfaction during pregnancy, evidence of variability in couple sexual satisfaction during pregnancy indicates that sexual dissatisfaction in pregnancy does not apply to all. Subsequently, the current study examined whether a nationally representative U.S. sample of wives and husbands (N = 523 couples) fell into subgroups in terms of their sexual satisfaction during pregnancy and to what degree biopsychosocial factors distinguish potential subgroups. Latent profile analyses, adjusted for pregnancy-related biological factors, indicated that couples could be classified into two subsets – a larger subset of couples where wives and husbands were satisfied with sex overall (79%) and a smaller subset where wives and husbands were neutral about satisfaction with sex (21%). Lower depressive symptoms among wives was associated with a greater likelihood of being in the more satisfied subset over the less satisfied subset – the only significant group membership predictor among a variety of other factors. Implications include notions that couples and practitioners should consider women’s depressive symptoms throughout pregnancy in addition to the perinatal period, and that most U.S. newly married pregnant couples do well navigating sexual satisfaction challenges during pregnancy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".