Gender Moderates the Partial Mediation of Impulsivity in the Relationship Between Psychiatric Distress and Problematic Online Gaming: Online Survey
Bibliographic record
Abstract
BACKGROUND: Research has shown that some individuals can develop problematic patterns of online gaming, leading to significant psychological and interpersonal problems. Psychiatric distress and impulsivity have been suggested to contribute to problematic online gaming (POG). OBJECTIVE: This study aimed to investigate the potential mediating or moderating mechanisms of impulsivity and gender-related differences in possible associations between psychiatric distress and POG. METHODS: A total of 596 matched female and male participants, ranging in age from 14 to 38 years (mean 21.4, SD 4.5), were chosen from a large cross-sectional, nationwide Hungarian online gaming sample. Participants completed online questionnaires about self-reported impulsivity, psychiatric distress, and POG. RESULTS: Psychiatric distress directly predicted POG, and impulsivity partially mediated the relationship between psychiatric distress and POG. However, this mediation effect was found only for the impatience factor of impulsivity. Impulsivity did not moderate the relationship between psychiatric distress and POG. A moderating effect of gender was not found in the direct relationship between psychiatric distress and POG. However, a moderated mediation analysis revealed that impatience mediated the association between psychiatric distress and POG in males, whereas the indirect effect of impatience was not significant in females. CONCLUSIONS: The results of this work highlight gender-related difference among online gamers in the mediation effect of impulsivity between psychiatric distress and POG and provide novel insights regarding clinical implications for preventing or treating POG.
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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 | 0.000 |
| 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) | 0.003 | 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".