Technology-mediated sexual interaction and relationships: a systematic review of the literature
Bibliographic record
Abstract
In the previous decade, researchers have increased their attention to people's engagement in technology-mediated sexual interaction (TMSI) – the exchange of sexual material via computer and Internet technologies. Inconsistent terminology and measurement across studies, however, has hindered the development of a cohesive knowledge base to inform sexual and relationship clinicians and educators. In this paper, we provide a critical review of the current state of the research on TMSI to identify gaps, clarify terminology, and synthesize current findings in the research. To do this, we conducted a systematic search of empirical literature focused on sexting, cybersex, and phone sex (terms that are consistent with TMSI). To meet inclusion criteria, authors of the studies were required to report on the prevalence of sexting, cybersex, and phone sex in adolescent and/or adult populations. Our findings indicate that TMSI is more prevalent among adults relative to adolescents. We also found that a majority of the studies focused on problematic use of TMSI (e.g. “cybersex addiction”); with comparatively less research examining normative TMSI. Our findings also indicate that many of the studies we systematically reviewed did not specify or differentiate between relationship types in their analyses. We conclude by discussing trends in the results and gaps in the literature that are pertinent to sexual and relationship researchers and clinicians.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".