The Relationship Between Digital Game Addiction and Loneliness and Social Dissatisfaction in Adolescents
Bibliographic record
Abstract
Background In this study, it was aimed to examine digital addiction, loneliness and social dissatisfaction among adolescents studying in Adıyaman, Turkey, and to determine the relationship with each other. Methodology Digital Game Addiction Scale for Children (DGASFC) and Loneliness and Social Dissatisfaction Questionnaire (LSDQ) were administered to 634 middle and high school students. A questionnaire form was used as a data collection tool. Results DGASFC scores and LSDQ scores were found to be higher in males, in high school students, in those whose parents' education level was high school or above, in those whose parents lived separately, in those with good economic status, in those who were younger, and in those who were not restricted by their families. A significant positive correlation was found between DGASFC and LSDQ scores. Conclusions Digital addiction should be followed closely in terms of accompanying disorders or pathologies that predispose to it. In our study, it was found that digital game addiction, loneliness and social dissatisfaction decreased with age. However, this applies separately to middle school and high school groups. Because, despite their older age, high school adolescents have been found to be more digitally dependent, lonely and socially dissatisfied than secondary school students. Contrary to the studies in the literature, the risk of digital addiction, loneliness and social dissatisfaction was found to be low in those with low economic status.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".