The relationships between the digital game addiction, alexithymia and metacognitive problems in adolescents
Bibliographic record
Abstract
The relationships between the digital game addiction, alexithymia and metacognitive problems in adolescentsErgen yaş grubunda dijital oyun bağımlılığı, aleksitimi ve üst biliş problemleri arasındaki ilişkinin incelenmesi SUMMARY Objective: Digital game addiction has become a diffuse problem among adolescents.The aim of this study is to investigate the relationships between digital game addiction, alexithymia personality traits and metacognitive problems in adolescents.Method: 664 adolescents (51% male, n=339, 49% female, n=325) from three secondary school in Istanbul were included in this study.The mean age of male and female participants was 12.89±1.29,and 12.58±1.53respectively.Digital game addiction scale for children (DGASFC), 20 item Toronto alexithymia scale (TAS-20), and the metacognition questionnaire for children and adolescents (MCQ-C) were applied to participants.The correlation coefficients between the scales were analyzed with Spearmen's rank order correlation test.The predictability of TAS-20 and MCQ-C subscale scores, gender and age on the status of digital game addiction was tested with binary logistic regression analysis.Results: There were positive correlations between DGASFC and TAS-20 total (r=.275),factor 1 (r=.250),factor 2 (r=.159), factor 3 (r=.175)scores, and MCQ-C total (r=.180) and factor 1 (r=.109)scores.Results of the binary regression analysis revealed that TAS-20 factor 1 and factor 3, and MCQ-C factor 1 scores, and the gender predict the status of digital game addiction, significantly.Discussion: It is suggested that addressing the problems of identifying and expressing the emotions, and metacognitive problems may increase the treatment success of the adolescents presenting 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".