Towards an Enriched Understanding of People Who Experience Problem Video Gaming
Bibliographic record
Abstract
Introduction: Video game addiction, also known as problem video gaming, is making headlines across the globe. Yet, our understanding of this relatively new phenomenon is limited. The purpose of this thesis is to develop an enriched understanding of people who experience problem video gaming. Methods: Two studies and a synthesis paper were completed. 1) A quantitative secondary analysis of survey data to determine the prevalence and correlates to problem video gaming. 2) A qualitative study using interviews and activity logs to understand the lives of people who experience problem video gaming from their own perspectives. 3) A synthesis of the two studies to facilitate a new discourse that broadens our understanding of problem video gaming. Results: Descriptive statistics found that 11.6% of youth experience problem video gaming. Logistic regressions revealed that the strongest predictors of problem video gaming were: being male, scoring lower in mental health status, playing more hours per day, being a problem gambler, receiving less parental monitoring, scoring lower in school subjective social status, not living in the East region of Ontario, and not working outside of the home. People who experienced problem video gaming described playing video games as a meaningful and purposeful activity. A model that explains the push and pull influences on the amount of gaming is described. The synthesis of these results demonstrated that problem gaming behaviours are influenced by issues arising not only in the individual, but also in the interpersonal and environmental circumstances. Significance of Findings: This thesis fosters new insights into the complexity of problem video gaming by explicating the interactions between the individuals who experience this, their interpersonal influences, and their environments. Consequently, the findings point to the importance of interpersonal and/or environmental issues which may have resulted in a misrepresentation of some gamers as being “addicted”.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".