Measurement, Profiles, Prevalence, and Psychological Risk Factors of Problematic Gaming Among the Turkish Community: A Large-scale National Study
Bibliographic record
Abstract
Abstract The present study investigated the prevalence, the potential different profiles, and the associated psychological factors of disordered gaming using data from a large-scale epidemiological study (TURBAHAR [Turkey’s Addiction and Mental Health Risk Profile Map Project]) carried out in Turkey in 2018 with 24,494 participants aged 18–81 years. Participants completed a comprehensive survey comprising a demographic questionnaire, Gaming Addiction Risk Questionnaire, Brief Symptom Inventory, Personal Well-Being Index Adult Form, Positive and Negative Affect Schedule, Toronto Alexithymia Scale, and Experiences in Close Relationships-Revised Scale. Latent class analysis showed the existence of eight different game profiles, which differed in relation to the intensity and specific features of the behavior. Results showed that 1.6% of the participants were problematic gamers. Being male, being younger, lower education level, being single, using alcohol and cigarettes, psychiatric distress, positive and negative affect, and anxious adult attachment were positively associated with problematic gaming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".