Analysis of Loneliness Levels and Digital Game Addiction of Middle School Students According to Various Variables
Bibliographic record
Abstract
The aim of this study was to examine the loneliness levels of students at the middle school level and their digitalgame addictions in terms of various variables. The study group consisted of 404 volunteer students in 5th-8th gradein Kutahya, Turkey. As data collection tools, "Digital Game Addiction Scale", developed by Lemmens et al. (2009)and "UCLA Loneliness Scale" developed by Russell et al. (1980) were used. The SPSS package program was usedfor the analysis of the data. According to research findings, it was found that there was a statistically significantdifference between the levels of digital game addiction and loneliness according to participation status to sportsactivities (p<0,05). Moreover, it was found that the levels of both loneliness and digital game addiction of theparticipants to sports activity were lower than non-participating. When gender and age variables were examined, itwas determined that there was a statistically significant difference between digital game addiction, gender, and age(p<0,05), whereas there was no statistically difference between loneliness level and both variables (p>0,05). Inaddition, it was determined that there were a moderate level and positive relationship between the level of lonelinessand digital game addiction of middle school students.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".