Personality and impulsivity traits associated with problematic online gaming and poker playing
Bibliographic record
Abstract
Online gambling and gaming are associated with Problematic Usage of the Internet (PUI) in subgroups of individuals. This study aimed to assess how different personality dimensions are associated with PUI scores of Massively Multiplayer Online Role Playing Games (MMORPG) players and online poker players, and to characterize common and specific personality traits of both groups in their association with PUI. Participants (N = 1144) were recruited online and assessed with the Internet Addiction Test (IAT), the Big Five Inventory and the Short UPPS-P Impulsive Behavior Scale. Data were analyzed with multiple robust linear regression models. The first model tested the associations between personality and impulsivity traits with IAT scores, while controlling for age, gender, and type of online activity. The second model included interaction terms to assess whether these associations differed between MMORPG and poker players. In model 1, neuroticism, negative urgency, positive urgency and sensation seeking were significantly and positively associated with higher IAT scores after controlling for the other personality traits, age, gender, and type of online activity. Extraversion was negatively associated with IAT scores. In Model 2, no significant difference in how these personality traits relate to IAT scores was observed between the two groups. Results highlight that traits such as neuroticism, negative urgency, positive urgency, and sensation seeking constitute potential risk factors for PUI, while extraversion might constitute a protective factor against PUI. The identified associations could be useful in understanding players’ attitudes and supporting them in gaining insight into their difficulties.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".