The Association Between Screen Time and Outdoor Time on Adolescent Mental Health and Academic Performance: Evidence from Rural China
Bibliographic record
Abstract
Purpose: We examine how adolescent free time allocation-namely, screen time and outdoor time-is associated with mental health and academic performance in rural China. Methods: =13.22), with data collected from self-reported demographic questionnaires (to assess free time allocation), the Strengths and Difficulties Questionnaire (to assess mental health), and a standardized math test (to measure academic performance). We utilized a multivariate OLS regression model to examine associations between free time allocation and adolescent outcomes, controlling for individual and family characteristics. Results: Our sample's screen time and outdoor time both averaged around 1 hour. About 10% of the sample adolescents reported behavioral difficulties, while a similar percentage (11%) reported abnormal prosocial behaviors. Adolescents with higher levels of screen time (>2 hours) were 3 percentage points more likely to have higher levels of behavioral difficulties (p<0.001), indicating that excessive screen time was associated with worse mental health. Meanwhile, outdoor time was associated with better mental health, and positive correlations were observed at all levels of outdoor time (compared to no outdoor time, decreasing the likelihood of higher levels of behavioral difficulties by between 3 and 4 percentage points and of lower prosocial scores by between 6 and 8 percentage points; all p's<0.001). For academic performance, average daily screen times of up to 1 hour and 1-2 hours were both positively associated with standardized math scores (0.08 SD, p<0.001; 0.07 SD, p<0.01, respectively), whereas there were no significant associations between outdoor time and academic performance. Conclusion: Using a large sample size, this study was the first to examine the association between adolescent free time allocation with mental health and academic performance, providing initial insights into how rural Chinese adolescents can optimize their free time.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".