A LONGITUDINAL DYADIC ANALYSIS OF TECHNOLOGY MEDIATED SEXUAL INTERACTIONS IN LONG DISTANCE RELATIONSHIPS
Bibliographic record
Abstract
Couples in long distance romantic relationships (LDRR) infrequently have in-person sexual interactions with each other, which may have negative consequences for their sexual and relationship satisfaction and ultimately relationship stability. Technology mediated sexual interactions (TMSI) may be one way couples in LDRR achieve sexual satisfaction. The current study dyadically addressed the relationship between the frequency of TMSI, various mediums used for TMSI (video call, voice call, texting, social media), and sexual satisfaction among long-distance couples. Participants were mixed-sex couples (N = 73) in LDRR who completed online questionnaires every two months for six months. Data were analysed using Multilevel Modeling (MLM) and the Actor-Partner Interdependence Model for both contemporaneous and time-lagged analysis. Results indicated that individuals’ frequency of TMSI was positively associated with their own contemporaneous sexual satisfaction and their partner’s subsequent sexual satisfaction. Contemporaneous analysis revealed that women’s frequency of TMSI using texting and social media was positively associated with their own sexual satisfaction while women’s frequency of voice calling for TMSI was positively associated with men’s sexual satisfaction. Results also indicated a significant negative effect of women’s frequency of use of video calling on men’s contemporaneous sexual satisfaction and women’s subsequent sexual satisfaction. Findings suggest that TMSI is associated with sexual satisfaction of couples in LDRR and that certain mediums of TMSI may be more beneficial than others. Individuals and their partners navigating the challenges of a LDRR may find that TMSI fosters sexual satisfaction, a central dimension of relationship quality and stability. Limitations and future directions 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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".