How do passion for video games and needs frustration explain time spent gaming?
Bibliographic record
Abstract
Research applying self-determination theory and the dualistic model of passion (DMP) has shown video games may satisfy basic psychological needs (i.e., competence, autonomy, and relatedness) and be identified as a passion. The DMP distinguishes between healthy or harmonious passion and problematic or obsessive passion (OP), with the latter reflecting an overreliance towards one's passion to obtain needs satisfaction. The experience of daily obstructions to needs satisfaction, or needs frustration (NF), may facilitate such an overreliance. This study explored how NF and both types of passion explain the amount of time that university students spend gaming. The overall association between NF and time spent gaming was not significant. However, for video game users with low levels of OP for gaming, there was a significant negative association between NF and time spent gaming. Additionally, evidence of a mutually reinforcing association between NF and OP for gaming indicates that a vicious cycle exists, whereby a strong OP for gaming predicts and is reinforced by greater NF. The theoretical implications are discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".