Examination of the Associations between Digital Game Addiction, Abilities of Reading Mind in the Eyes and Alexithymia: An Adolescent Sample from Ordu Province
Bibliographic record
Abstract
Objective: The aim of this study is to examine the relationships between digital game addiction, abilities of reading mind in the eyes and alexithymia personality characteristics. Method: Six hundred and sixty-two adolescents between the ages of 15 and 17 from two secondary schools in Ordu Province (n = 358 females, 57%; n = 286 males, 43%) were included in the study. Digital game addiction scale for children (DGASFC), Child Form of Reading the Mind in the Eyes Test (Eyes Test) and 20 item Toronto alexithymia scale (TAS-20) were administered to participants. The scale scores of female and male participants were compared by independent sample t test. Correlations between the scales were analyzed by Pearson product moment correlation test. The predictive effects of skills of reading mind in the eyes, alexithymia, gender and age on the development of digital game addiction were evaluated by multivariate linear regression analysis. Results: Digital game addiction scores of males were significantly higher than females. The scores of DGASFC were negatively correlated with the scores of Eyes Test and positively correlated with total scores of TAS-20 and its subscales’ scores, significantly. Regression analysis revealed that the scores of Eyes Test, TAS-20 factor 1 and factor 3, and gender were significantly predict the digital game addiction levels. Conclusions: The therapeutic interventions to improve abilities of reading mind in the eyes, identifying emotions, and empathic thinking skills may be beneficial for the adolescents which present with digital game addiction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".