Gender Differences in Desire Discrepancy as a Predictor of Sexual and Relationship Satisfaction in a College Sample of Heterosexual Romantic Relationships
Bibliographic record
Abstract
The authors examined desire discrepancy and its effect on sexual and relationship satisfaction in a sample of 133 heterosexual couples attending a midsize university. Couples were required to be in a relationship for at least 1 year (M = 4.32 years, SD = 3.13 years); 23.7% of the couples were cohabitating. Hierarchical multiple regression results indicated that higher desire discrepancy scores significantly predicted women's (but not men's) lower sexual satisfaction after controlling for relationship satisfaction. Higher desire discrepancy scores significantly predicted men's (but not women's) lower relationship satisfaction after controlling for sexual satisfaction. The authors assessed gender differences using a mixed model with the dyad and gender as factors and satisfaction as the outcome. Although gender difference patterns appeared in the regression models, the differences were nonsignificant within each couple in the extent to which desire discrepancy affected sexual and relationship satisfaction. These findings suggest moving away from focusing on only one partner with low desire and shifting attention to the dyad's interaction. Also, the way in which desire discrepancy affects sexual and relationship satisfaction deserves consideration. Therapeutic implications and study limitations are discussed.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".