Bidirectional associations between video game playing and ADHD symptoms among school-aged children
Bibliographic record
Abstract
Past research has suggested associations between gaming and symptoms of attention-deficit/hyperactivity disorder (ADHD) among children and adolescents. Yet, little research has been conducted to clarify the directionality of this association during middle childhood when ADHD issues typically emerge. Clarifying directionality is key to understanding whether gaming precedes and predicts ADHD symptoms, or the opposite. To shed light on this topic, this study investigates this association longitudinally during middle childhood. We employed a Random Intercept Cross-Lagged Panel Model to estimate longitudinal bidirectional associations between child gaming and ADHD symptoms from ages 6 to 10. Variables were derived from parent-reported child weekly hours of gaming and teacher-reported child ADHD symptoms. Data are from the Quebec Longitudinal Study of Child Development, a population-based cohort of Canadian children (N=1749). We hypothesized a bidirectional association. Our results revealed that higher levels of ADHD symptoms at age 6 predicted more time gaming at age 7. Likewise, more ADHD symptoms at age 7 predicted more gaming at age 8. However, later in development, this association reverses direction: higher levels of gaming at age 8 predicted more ADHD symptoms at age 10. Additional analyses of separated dimensions of ADHD revealed that associations with gaming were stronger for the hyperactivity/impulsivity dimension. These findings suggest that children with more ADHD symptoms tend to devote more time to gaming during the early years of middle childhood. In turn, this increase in gaming during early school years contributes to worsening ADHD symptoms later in development, particularly hyperactivity/impulsivity symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".