Prevalence and correlates of screen time among Brazilian adolescents: findings from a country-wide survey
Bibliographic record
Abstract
The purpose of this study was to evaluate the distribution, prevalence, and correlates of excessive screen time (>2 h/day) among Brazilian adolescents. The Study of Cardiovascular Risks in Adolescents (ERICA) is a national, school-based, cross-sectional multicenter study. Information about time spent in front of screens was assessed by questionnaire. Poisson regression models were used to examine the associations between following correlates (region, sex, age, skin color, income, Internet access, and number of TVs at home) and excessive screen time. A total of 66 706 Brazilian adolescents (aged 12-17 years) were included. The overall mean time in front of screens was 3.25 h/day (95% confidence interval (95%CI): 3.20-3.31) and the prevalence of excessive screen time was 57.3% (95%CI: 55.9-58.6). Moreover, excessive screen time also differs across Brazilian regions, being higher in Southeast and South, respectively. In adjusted models stratified by region, the socioeconomic status was associated with excessive screen time in North, Northeast, and Midwest. In all regions, having a computer with Internet access was associated with higher prevalence of excessive screen time. In conclusion, prevalence of excessive screen time in Brazilian adolescents is high. It presents regional variations and facility for Internet access.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".