Association between Recreational Screen Time and Sleep Quality among Adolescents during the Third Wave of the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
The study objective was to verify whether recreational screen time was associated with sleep quality among adolescents during the third wave of the COVID-19 pandemic in Canada. Data collection took place in four high schools in the region of Chaudière-Appalaches (Quebec, Canada) from the end of April to mid-May 2021. Recreational screen time and sleep quality were measured using the French versions of validated questionnaires specifically designed for adolescents. A total of 258 adolescents (14−18 years; 66.3% girls) answered the online survey. Adolescent boys had a higher total mean recreational screen time (454.3 ± 197.5 vs. 300.5 ± 129.3 min/day, p < 0.0001) and a higher total mean sleep quality score (4.2 ± 0.9 vs. 3.9 ± 0.8, p = 0.0364) compared to girls. Recreational screen time (β = −0.0012, p = 0.0005) and frequency of concurrent screen use (sometimes: β = −0.3141, p = 0.0269; often: β = −0.4147, p = 0.0048; almost always or always: β = −0.6155, p = 0.0002) were negatively associated with sleep quality while being a boy (β = 0.4276, p = 0.0004) was positively associated with sleep quality and age (p = 0.6321) was not. This model explained 16% of the variance in adolescents’ sleep quality. Public health interventions during and after the COVID-19 pandemic should target recreational screen time, concurrent screen use and especially girls to possibly improve sleep quality and promote adolescents’ physical and mental health.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".