From one year to the next: Video gaming life‐style predicts subsequent psychosocial risk in adolescent boys and girls
Bibliographic record
Abstract
AIM: The ubiquitous societal presence of screens and gaming software is ever growing in popularity. However, can this recreational life-style pose risks for children's psychosocial adjustment? We thus examine associations between gaming life-style and later psychosocial development. METHODS: The study sample originates from the 2120 boys and girls from the Quebec Longitudinal Study of Child Development birth cohort. At age 12, 625 boys and 702 girls from the original sample self-reported the number of hours engaged in video gaming per week and 1 year later, they self-reported psychosocial outcomes. RESULTS: Among boys, higher weekly video gaming frequency at age 12 forecasted increases in subsequent reactive aggression, emotional distress and ADHD symptoms at age 13. For girls, higher weekly video gaming frequency at age 12 predicted increases in subsequent reactive aggression and ADHD symptoms at age 13. No association between video game use and emotional distress was found for girls. CONCLUSION: In both boys and girls, a more intense video gaming life-style predicted subsequent risks for reactive aggression and ADHD symptoms, compared with their same sex counterparts reporting less intensity. For boys only, video gaming was associated with subsequent reactive emotional distress, likely due to the gender differences in violent content. Our data were collected at a time when there were less versatile screen-based technologies; therefore, our findings can be interpreted as very conservative compared to current estimates. Paediatric professionals and allied disciplines must take preventive measures to ensure that parents are aware of the risks associated with excessive use by their sons and daughters.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".