Influences on sexting in an intimate relationship: Motivations, risks, communication, personality traits, and relationship variables
Bibliographic record
Abstract
People in intimate relationships sext more than those in casual relationships or those who are single. Past research indicates a differential pattern in sexting behaviour based on relationship status, so a closer look at how sexting might serve the sexual and relationship needs of those in intimate relationships is warranted. In this study, we used quantitative and qualitative methods to investigate several factors that influence sexting behaviour within intimate relationships. Participants ( N = 771; 76% women, 23% men, 0.5% non-binary) completed an online questionnaire about personality traits, satisfaction, communication, trust, commitment, motivations, and risks that included open-text space to elaborate responses. This study used incentive motivational theory as a guide. Results suggest that for those in committed relationships, sexting is a way to have intimate connection that is mediated by technology. Participants were motivated to sext for several reasons, but most commonly for sexual gratification. Participants did not perceive there to be any risks to sexting with their partner. Sexting was found to be positively related to relationship and sexual satisfaction, commitment, erotophilia, and sexual sensation seeking. The therapeutic implications of these results 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".