Computer/gaming station use in youth: Correlations among use, addiction and functional impairment
Bibliographic record
Abstract
OBJECTIVE: Computer/gaming station use is ubiquitous in the lives of youth today. Overuse is a concern, but it remains unclear whether problems arise from addictive patterns of use or simply excessive time spent on use. The goal of the present study was to evaluate computer/gaming station use in youth and to examine the relationship between amounts of use, addictive features of use and functional impairment. METHOD: A total of 110 subjects (11 to 17 years of age) from local schools participated. Time spent on television, video gaming and non-gaming recreational computer activities was measured. Addictive features of computer/gaming station use were ascertained, along with emotional/behavioural functioning. Multiple linear regressions were used to understand how youth functioning varied with time of use and addictive features of use. RESULTS: Mean (± SD) total screen time was 4.5±2.4 h/day. Addictive features of use were consistently correlated with functional impairment across multiple measures and informants, whereas time of use, after controlling for addiction, was not. CONCLUSIONS: Youth are spending many hours each day in front of screens. In the absence of addictive features of computer/gaming station use, time spent is not correlated with problems; however, youth with addictive features of use show evidence of poor emotional/ behavioural functioning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".