Social media use and sleep health among adolescents in Canada
Bibliographic record
Abstract
INTRODUCTION: Public health concerns over the impact of social media use (SMU) on adolescent health are growing. We investigated the relationship between SMU and sleep health in adolescents in Canada aged 11 to 17 years. METHODS: Data from the 2017-2018 Health Behaviour in School-aged Children study were available for 12 557 participants (55.2% female). SMU was categorized by frequency of use (non-active, active and intense) and the presence of addiction-like symptoms (problematic). Mixed effects logistic regression models identified associations between SMU and seven sleep health indicators (insomnia symptoms, daytime wakefulness problems, screen time before bed, meeting sleep duration recommendations, sleep variability and late bedtime on school and non-school days). RESULTS: Compared to active SMU, non-active SMU was associated with better sleep indicators, except for insomnia symptoms. Intense SMU was associated with greater odds of having poor sleep health indicators (adjusted odds ratio [aORs] from 1.09 to 2.24) and problematic SMU with the highest odds (aORs from 1.67 to 3.24). Associations with problematic SMU were greater among girls than boys, including having a later bedtime on school days (aOR = 3.74 vs. 1.84) and on non-school days (aOR = 4.13 vs. 2.18). Associations between SMU and sleep outcomes did not differ by age group. CONCLUSION: Intense and problematic SMU were associated with greater odds of poor sleep health among adolescents in Canada, with stronger associations among girls than boys. Further research is needed to understand the mechanisms underlying associations between SMU and sleep to inform public health recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".