Alexithymia, internet addiction, and cyber-victimisation among high school students in Turkey: an exploratory study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: The study aims to explore the interrelationships among internet addiction, cyber-victimisation, and alexithymia in high school adolescents in Turkey, emphasising the role of gender. Materials & Methods: 305 participants were surveyed via Young's Internet Addiction Test – Short Form (YIAT-SF), Toronto Alexithymia Scale (TAS-20), and the Cyberbullying Scale. The influence of gender on alexithymia, particularly in identifying and describing feelings, and its effect on internet addiction and cyber-victimisation was evaluated by path analysis. Results: There was a moderate positive correlation between YIAT-SF and TAS-20 total scores (r = 0.385, p < 0.001). YIAT-SF and TAS-20 total scores were positively correlated with CVS score (r = 0.151, p = 0.008; r = 0.140, p = 0.015, respectively). The results revealed gender significantly affects alexithymia dimensions, particularly in difficulty identifying feelings (DIF) (β = 0.14, p = 0.010) and difficulty describing feelings (DDF) (β = 0.28, p < 0.001). Moreover, DDF was found to have a substantial impact on cyber-victimisation (β = 0.32, p < 0.001), and DIF significantly influenced internet addiction (β = 0.49, p < 0.001). Conclusions: The findings highlight the importance of considering gender-specific factors when addressing Internet addiction and cyber-victimisation. Gender differences in alexithymic traits highlight the need for specific preventive and therapeutic approaches that focus on emotional recognition and expression skills.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it