Examination computer gaming addiction, alexithymia, social anxiety, age and gender among children aged 8-12
Bibliographic record
Abstract
Objective: The aim of this study is to examine age, gender, alexithymia and social anxiety as predictors of computer gaming addiction among children. Method: The research was carried out on a total of 321 persons, 148 of whom were girls and 173 were boys. The ages of the participants were between 8 and 12 and their average age was 10.48. Research data were collected via Computer Gaming Addiction Scale for Children, Alexithymia Scale for Children, Social Anxiety Scale for Children and Personal Information Form. Parametric tests were used because it was found that the data of research were normally distributed and met the necessary assumptions for multiple regressions. The data obtained from the research were analyzed by Pearson Correlation Analysis and Hierarchical Regression Analysis method. Results: A positive correlation has been found between computer gaming addiction and alexithymia and social anxiety. In addition, age, gender, alexithymia and social anxiety were significantly predictive of computer gaming addiction at 24.1%. In the research, it was identified that the most effect in explaining computer gaming addiction was alexithymia, followed by gender and social anxiety. Discussion: It is found out that gender, alexithymia and social anxiety affect computer gaming addiction in children significantly. The results of research were discussed in the light of the related literature and suggestions were made to the researchers and experts studying in the field.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".