A contemporary exploration of the relationship between attachment and sexual satisfaction: the role of technology-mediated sexual interaction
Bibliographic record
Abstract
The evolution of technology has transformed the way young adults develop and maintain relationships, including their sexuality. Since many young adults report low sexual satisfaction, it is important to understand what contributes to these negative experiences. Several studies have identified that attachment anxiety and avoidance are related to lower sexual satisfaction. However, few studies have considered technology-mediated sexual interactions (TMSI) – and the motives for doing so – in the associations between attachment and sexual satisfaction. This study explored the mediating and moderating role of TMSI (frequency and motives) in these associations in a sample of 478 young adults (Study 1) and 142 couples (Study 2). Results showed that attachment anxiety was related to a higher TMSI frequency and engaging in TMSI for avoidance motives. Engaging in TMSI for approach motives was related to higher sexual satisfaction, while engaging in TMSI for avoidance motives was related to lower sexual satisfaction. Finally, engaging in TMSI to manage distance moderated the association between attachment avoidance and sexual satisfaction. These results suggest that beyond the role of attachment anxiety and avoidance, contemporary factors related to digital technologies, such as sexting frequency and motives, are related to sexual satisfaction.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 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".