Mediating role of alexithymia in relationship between cyberbullying and psychotic experiences in adolescents
Bibliographic record
Abstract
BACKGROUND: Today, addressing issues related to the use of virtual space is of paramount importance due to its significant impact on mental well-being. This is especially crucial when the research community consists of teenagers who are cyber bullies or their victims who have higher vulnerability. The aim of the present study was to investigate the mediating role of alexithymia in the relationship between cyberbullying and psychotic experiences in adolescents. METHODS: The research method employed in this study was correlational, and the study population consisted of all male and female middle school students in Tehran during the 2022-2023 academic years. As for data collection, the Cyber-Bullying/Victimization Experiences questionnaire, Community Assessment of Psychic Experiences, and the Toronto Alexithymia scale were applied. A total of 602 samples were gathered by using multi-stage cluster sampling from Tehran in Iran. Four selection of the sample, the regions in Tehran were selected randomly according to the geographical directions of them and then some schools and classes were chosen randomly. Sample was included in the analysis after data entry into SPSS software and subsequent structural equation modeling using AMOS software. RESULTS: According to the findings, cyberbullying (β = 0.11,p < 0.05) and cyber victimization(β = 0.41, p < 0.001) were significant predictors of psychotic experiences. Alexithymia partially mediated the relationship between cyberbullying and psychotic experiences with the mediation effect of 0.28 and cyber victimization and psychotic experiences with the mediation effect of 0.18. CONCLUSIONS: These findings underscore the importance of identifying cyber victims or cyberbullies in order to prevent alexithymia and psychotic experiences in future, in order to prevent more serious problems and becoming psychotic. TRIAL REGISTRATION: The goals and conditions of this research were investigated and approved by the Ethics Committee of Alzahra University in Tehran (code: ALZAHRA.REC.1402.055) on 13th September 2023.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".