Changes in Screen Time Behaviors from Before (2019) to After (2022) the COVID-19 Pandemic Among Brazilian Adolescents
Bibliographic record
Abstract
OBJECTIVE: Compare prepandemic (2019) and postpandemic (2022) engagement in five screen-based activities (studying, working, watching videos, playing video games, and using social media/chat applications) among independent samples of Brazilian adolescents using a repeated cross-sectional design; and 2) Examine within-individual changes in these same screen-based activities over the same period using a repeated cross-sectional study with a nested cohort. METHODS: Data were collected in 2019 and 2022, involving a total of 2008 adolescents who participated in the repeated cross-sectional study, with 333 forming a nested cohort sample. Zero-inflated multilevel gamma regression models and multilevel linear models were used to analyze the data. RESULTS: In the repeated cross-sectional analysis, adolescents spent more minutes per day in 2022 versus 2019 for studying (+21.3 minutes; 95% CI: 11.0, 31.6), watching videos (+12.8 minutes; 95% CI: 1.1, 24.5), and playing video games (+22.9 minutes; 95% CI: 12.8, 33.1). The longitudinal analysis revealed significant average daily increases from 2019 to 2022 in studying (+53.8 minutes; 95% CI: 34.7, 72.9) and working (+130.2 minutes; 95% CI: 110.4, 149.9). For these same adolescents, significant decreases were observed for watching videos (-26.4 minutes; 95% CI: -48.0, -4.9) and playing video games (-28.6 minutes; 95% CI: -46.2, -11.8). Social media use remained stable. CONCLUSIONS: Screen time (ST) among Brazilian adolescents was higher in 2022 compared to 2019, with increases in studying, working, watching videos, and playing video games. Longitudinal data indicated a shift from recreational ST to educational and work-related ST. These findings highlight the need for targeted interventions to promote balanced ST and mitigate potential negative health impacts.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".