Associations of Passive and Mentally Active Screen Time With Perceived School Performance of 197,439 Adolescents Across 38 Countries
Bibliographic record
Abstract
OBJECTIVE: To examine the associations of passive (ie, television) and active (ie, electronic games, computer use) screen time (ST) with perceived school performance of adolescents across gender. METHODS: Data were from the 2014 Health Behaviour in School-aged Children survey conducted across 38 European countries and Canada. Perceived school performance was assessed using an item and dichotomized as high (good/very good) versus the remainder (average/below-average as reference). Participants reported hours per day of time spent watching television, playing electronic games, and using a computer in their free time. Multilevel logistic regression was used to estimate the associations. RESULTS: A total of 197,439 adolescents (average age 13.6 [standard deviation 1.63] years; 51% girls) were analyzed. Multivariable modeling showed that engaging in >2 h/d of ST was progressively and adversely associated with high performance in both boys and girls. Adolescents reporting >4 h/d of television time (≤1 h/d as reference) had 32% lower odds in boys (odds ratio [OR] 0.68; 95% confidence interval [CI]: 0.65-0.71) and 39% lower odds in girls (OR 0.61; 95% CI, 0.58-0.65) of reporting high performance. Playing electronic games for >4 h/d was associated with high performance with odds being 38% lower in boys (OR 0.62; 95% CI, 0.59-0.66) and 45% lower in girls (OR 0.55; 95% CI, 0.52-0.57). Sex differences in the estimates were mixed. CONCLUSIONS: High screen use, whether active or passive, was adversely associated with perceived high school performance, with association estimates being slightly stronger in girls than boys, and for mentally active than passive screen use. Discouraging high levels of screen use of any type could be beneficial to school performance.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".