Attachment style and distress in couples experiencing sexual desire discrepancy
Bibliographic record
Abstract
Intimate partners’ sexual desire for each other can fluctuate throughout the course of their relationship, and greater difference between partners’ habitual level of sexual desire (i.e., sexual desire discrepancy [SDD]) is linked to lower sexual and relationship satisfaction ( Mark, 2015 ). Although some couples view SDD as a natural and normal part of a sexual relationship, others experience significant distress and may seek therapy. Based on a prominent literature on attachment among couples ( Birnbaum & Reis, 2019 ; Mark et al., 2018 ; Mikulincer & Shaver, 2007 ), the current study incorporated an actor-partner interdependence model to examine the dyadic associations between insecure attachment style (i.e., anxious, avoidant) and sexual desire among couples experiencing distressing or non-distressing levels of SDD. Couples ( N = 202; 51% female, 48% male, 1% different gender; M = 28 years old, SD = 5.05) were recruited through social media (e.g., Instagram, Twitter) and completed a survey assessing sexual distress, adult attachment style, and sexual desire. Although anxious and avoidant attachment did not moderate an association between SDD and sexual distress, higher levels of avoidant attachment were associated with sexual distress for the individual, but not with their partner’s distress. Findings suggest attachment may be essential for understanding individuals’ experience of sexual distress when coping with discrepant levels of sexual desire in romantic relationships.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".