Pursuing virtual perfection: Preoccupation with failure mediates the association between internalized parental criticism and gaming disorder
Bibliographic record
Abstract
The present study examines the potential roles of perfectionism and reactions to failure in understanding gaming disorder. Specifically, we investigate whether parental perfectionism predisposes players to risk of gaming disorder through internalized perfectionism and maladaptive reactions to failure. Hungarian gamers (N = 2,097, 88.5% male, Mage = 26.2 years, SD = 6.8) completed an online survey measuring perfectionism (parental and self-oriented), reactions to failure in gaming, and gaming disorder. Initially, we developed the Reactions to Failure in Gaming Scale and established its psychometric properties. Subsequently, we constructed a path model using a structural equation modeling technique. Parental criticism was associated with over-engagement with failure via the indirect path of self-critical perfectionism, which was positively associated with gaming disorder. In addition, higher parental expectations were associated with disengagement from failure via narcissistic perfectionism, while parental criticism was associated with disengagement through self-critical perfectionism. The model explained a substantial proportion (42%) of the total variance of gaming disorder, indicating that parental criticism and self-critical perfectionism have key roles in ruminative responses to failure in gaming. These results suggest that critical parental and personal attitudes towards performance and over-engagement with failure make fundamental contributions to the development of addictive gaming behaviors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".