Investigating the Structural Relationships of the Tendency to Addiction to Mobile Phone-Based Social Networks in Students, Based on Early Maladaptive Schemas Mediated by Alexithymia in Students
Bibliographic record
Abstract
The aim of current research was to investigating structural relationships of addiction to mobile phone-based social networks in students, as influenced by early maladaptive schemas mediated by alexithymia. In terms of purpose, this research was applied research, while in terms of method, it was correlational studies. The statistical population of this survey comprised all individuals between the ages of 18 and 30 who resided in Tehran and Isfahan in 2021. The available sampling method was employed to select a sample of 524 individuals, which included 381 girls and 143 boys. Mobile Social Network Addiction (SNA), Toronto Alexithymia (TAS_20), and the Young Schema Questionnaire-Short Form3 (YSQ-S3) were completed by the participants. The research data was analyzed using structural equation modeling and the R-4.2 and SPSS-26 software. Based on research findings, variable of maladaptive schemas was able to predict the tendency to addiction to social networks in a positive and significant manner. However, there was no significant relationship between Alexithymia and the propensity to develop an addiction to social media. Results showed that Alexithymia does not play a mediating role in predicting tendency to addiction to social networks based on maladaptive schemas. It is recommended that psychologists and counselors consider the role of early maladaptive schemas in the prevention, control, and treatment of addiction to social networks in light of the results obtained.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".