Reliability and Validity of the Behavioral Addiction Measure for Video Gaming
Bibliographic record
Abstract
Most tests of video game addiction have weak construct validity and limited ability to correctly identify people in denial. The purpose of the present research was to investigate the reliability and validity of a new test of video game addiction (Behavioral Addiction Measure-Video Gaming [BAM-VG]) that was developed in part to address these deficiencies. Regular adult video gamers (n = 506) were recruited from a Canadian online panel and completed a survey containing three measures of excessive video gaming (BAM-VG; DSM-5 criteria for Internet Gaming Disorder [IGD]; and the IGD-20), as well as questions concerning extensiveness of video game involvement and self-report of problems associated with video gaming. One month later, they were reassessed for the purposes of establishing test-retest reliability. The BAM-VG demonstrated good internal consistency as well as 1 month test-retest reliability. Criterion-related validity was demonstrated by significant correlations with the following: time spent playing, self-identification of video game problems, and scores on other instruments designed to assess video game addiction (DSM-5 IGD, IGD-20). Consistent with the theory, principal component analysis identified two components underlying the BAM-VG that roughly correspond with impaired control and significant negative consequences deriving from this impaired control. Together with its excellent construct validity and other technical features, the BAM-VG represents a reliable and valid test of video game addiction.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".