Addiction-related behaviours among university students: Relationships between problem gambling, problematic video game playing, excessive internet use, and emotional intelligence (EI).
Bibliographic record
Abstract
The present study expanded upon the work of Parker et al. (2008, 2012) by confirming the links among addiction-related behaviours, including problematic gambling, video game play, and internet use (i.e., dysfunctional preoccupation), and exploring the relationship between these addiction-related behaviours and the four dimensions of the Bar-On model of emotional intelligence (EI; Bar-On, 1997) among 922 (231 men, 690 women) undergraduate students between 17 and 24 years of age (M = 19.42, SD = 1.64). Participants completed an online questionnaire package that included the Emotional Quotient Inventory: Short Form (EQ-i:S; Bar-On, 2002), a gambling activities checklist, the South Oaks Gambling Screen - Revised for Adolescents (SOGS-RA; Winters et al., 1993), a video game activities checklist, the Problematic Video Game Playing Scale (PVGS; Salguero & Moran, 2002), an internet mediated activities checklist, and the Internet Addiction Test (IAT; Young, 1998). The results provided support for the dysfunctional preoccupation construct among university students. This construct reflects the relationships among addiction-related behaviours, and provides some support for a common underlying etiology in addiction-related behaviours. The four dimensions of EI were stronger predictors of addiction-related behaviours among females than males. Perceived stress management abilities were the most reliable predictor of addiction-related behaviour symptomatology among both males and females, whereas interpersonal abilities were found to be the strongest predictor of addiction-related behaviours among females. The observed relationship between affect regulation abilities and addiction-related behaviours among females is consistent with previous work that has found affect-regulation deficiencies to be risk factors in the development of addiction-related behaviours.
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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 | 0.003 |
| 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 | 0.001 | 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".