Examining the role of psychological needs and passion in problem video gaming
Bibliographic record
Abstract
Video game play is a pervasive behavior, and researchers suggest there may be both adaptive and maladaptive consequences of gaming. Using Self-Determination Theory and the Dualistic Model of Passion frameworks, the purpose of this study was to examine the relationships between passion, psychological needs, and problematic gaming. Across two studies and an integrative data analysis (IDA) that pooled the data of Studies 1 and 2, we hypothesized that (H1) greater psychological need frustration would be indirectly related to greater problematic gaming through greater obsessive passion and that (H2) greater psychological need satisfaction would be indirectly related to lower problematic video gaming via greater harmonious passion. We also tested alternative models in which passion (harmonious and obsessive) were related problematic gaming through to psychological needs (satisfaction and frustration). Across the two studies and IDA, we found that psychological need frustration was associated problematic gaming via obsessive passion, supporting our first hypothesis. Similarly, our alternative model also indicated that obsessive passion was related to problematic gaming via psychological need frustration in all three studies. In contrast, we did not find support for hypothesis two in either study and the IDA as psychological need satisfaction was not associated with lower problematic gaming via harmonious passion. In fact, we found that in Studies 1 and 2 as well as the IDA, psychological need frustration was related to problematic gaming through harmonious passion. Our results suggest that psychological need frustration and obsessive passion may be more important indicators of problem gaming compared to psychological need satisfaction and harmonious passion. Therefore, efforts to mitigate psychological need frustration in daily life and obsessive passion for gaming may be critical for reducing problem gaming.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".