Modeling the structural relationships of internet addiction based on executive functions and problems interpersonal: the mediating role of Alexithymia
Bibliographic record
Abstract
Introduction: Internet addiction is a type of impulse control disorder that refers to excessive use of the internet and can affect other social activities. Aim: Purpose of this study was to determine the structural relationships of executive functions, interpersonal problems with internet addiction through the mediation of gossip and excitement. Method: The descriptive-correlation research method was based on the modeling method of regression structural equations. In order to achieve this goal, out of all seventh grade female students in Tehran's 1, 7 and 8 grades in the 2018-2019 academic year, 260 people were randomly selected as clusters and were referred to the Rescue Executive Function Questionnaire (2013(, Barkham, Hardy, & Startup (1988), intermediate problems, Toronto Gabby, Parker, and Taylor (1994), and Kimberly Young's Internet Addiction Scale (1996). Structural equation statistical method was used to analyze the data. Also, all analyzes were performed using SPSS 24 and Amos 23 software. Results: The results showed that there was a significant significant direct and negative effect between interpersonal problems with internet addiction and a significant positive and positive effect between executive functions and internet addiction (P<0.001). Emotional inadequacy of internet addiction had a significant direct and positive effect on students (P<0.001) and also emotional inadequacy played a mediating role in the relationship between executive functions and interpersonal problems with internet addiction (P<0.001). Conclusion: Based on the research findings, it can be concluded that the present research model had statistically acceptable fit indicators.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".