Sexting: A Digital Gateway to Understanding Risk Propensity and Alexithymic Traits
Bibliographic record
Abstract
Aim: The current study explores the patterns of sexting behaviour among individuals actively using online dating apps, with a focus on whether sexting can predict Alexithymic traits and risk-taking behaviours, while also delving deeper into the relationship between the different variables. Methods: This quantitative study employs convenience sampling to recruit 194 individuals, aged 18-25, who are active users of online dating apps across Chennai. The variables were quantified using standardised measures which include; Sexting Behaviours Scale (SBS), The Risky, Impulsive, & Self-destructive behaviour Questionnaire (RISQ), and Toronto Alexithymia Scale Questionnaire (TAS - 20) . The data obtained were then subject to correlation analyses using SPSS software. Results: The results of the study highlight significant relationships between sexting, alexithymia and risk taking behaviours. Findings show that individuals with high levels of alexithymia are much more likely to partake in sexting and the other array of risk taking behaviours as a form of coping mechanism. These results underscore the role of emotional dysregulation in driving risk - taking tendencies, providing us with valuable insights into the psychological factors influencing the behaviors. Conclusion: The findings of the study delve into the complex relationship between young adults’ risk taking behaviors, alexithymia and sexting behavior, with a focus on the domain of online dating. They contribute to the growing body of literature by emphasizing the psychological and behavioral implications of sexting in the digital age, thereby paving the way for developing targeted interventions to mitigate these risks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".