Self-Regulation as a Mediator of the Associations Between Passion for Video Games and Well-Being
Bibliographic record
Abstract
Video games can satisfy people's basic psychological needs of autonomy, competence, and relatedness. This may lead them to develop a passion for the activity, which can be harmonious or obsessive. These different types of passions are associated with different well-being outcomes: harmonious passion (HP) is associated with positive effects such as Satisfaction with Life (SWL), obsessive passion (OP) is associated with adverse effects such as psychological distress. Although time spent playing video games has sometimes been found to be a predictor of poor well-being, there is a lack of understanding in its role in explaining the relationship between passion and well-being compared with other factors. Self-regulation is an important factor in predicting habits, including video game play. In this cross-sectional study (N = 182), we investigated whether self-regulation or playtime better mediated the associations between different passion orientations and well-being (i.e., SWL, global subjective well-being, and psychological distress) among video game players. A path analysis revealed that people with higher HP for video games reported higher levels of self-regulation and those with higher OP for video games reported lower levels of self-regulation. Our findings also indicate that self-regulation provides a more comprehensive explanation for the relationship between passion and well-being. Overall, this study provides further support for the importance of self-regulation as a determinant of well-being in video game players rather than more arguably surface-level metrics such as time spent playing. These findings have implications for game developers and clinicians who design interventions for individuals who may experience unregulated video game play.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".