Technology-Mediated Sexual Interactions, Social Anxiety, and Sexual Wellbeing: A Scoping Review
Bibliographic record
Abstract
Technology-mediated sexual interactions (TMSI) are interpersonal exchanges via technology of self-created sexual material, including photos, videos, and auditory or text messages. There is little research on the factors that predict both TMSI experiences and their sexual wellbeing outcomes. Social anxiety is anxiety experienced in response to social or performance situations. From a cognitive-behavioural perspective, people higher in social anxiety may avoid TMSI, preventing positive or negative consequences. They also may use TMSI to avoid the anxiety caused by in-person sexual interactions, benefiting from access to sexual interactions while perpetuating anxiety about them. The purpose of this scoping review was to explore the role of social anxiety in TMSI and its sexual wellbeing outcomes. We executed a comprehensive search strategy across eight academic databases and searched reference lists of included articles. We included 19 articles written in English or French that had a human sample and were published between 1991 and 2021 and evaluated connections between social anxiety constructs (e.g., shyness, anxiety) and TMSI-related experiences (e.g., sexting, internet sex addiction). The pattern of results suggested that social anxiety constructs may predict some but not all forms of TMSI. Future research from a cognitive-behavioural perspective will expand knowledge on social anxiety, TMSI, and its sexual wellbeing outcomes.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".