Screen Use and Child and Adolescent Health in Canada: Triangulation of Evidence Assessing the State of the Effort
Bibliographic record
Abstract
BACKGROUND: Excessive screen use in children and adolescents is an increasingly prevalent, pervasive and pressing public health issue in Canada. This project investigated the 'state of the effort' in relation to screen use and healthy development in children and adolescents in Canada. METHODS: Five investigations were employed to identify, describe, interpret and triangulate the current evidence, including a national opinion survey, environmental scan, school board policy scan, network analysis and text mining analysis of Canadian news headlines. RESULTS: Screen use and healthy child development is an important issue for Canadian parents (n = 385), and they believe schools (79%), governments (73%) and technology companies (71%) have a responsibility to promote safe use. Most policy documents found were relatively recent (2023+) with both provincial (e.g., educational settings with a focus on personal digital devices) and national scope (e.g., protecting children regarding online programming, content and privacy). Of the 38 largest school boards in Canada, 84% ban or restrict students' unnecessary use of mobile devices during classroom/instructional time and 42% block or restrict access to social media platforms. Linked network analysis clusters included health, research, advocacy, law, policy, education, environment and physical activity, with most partnerships being weak, indicating a suboptimal connection among these sectors. Dramatic increases in related media headlines were found in 2024 (n = 1061) compared with 2023 (n = 400), and most lexicon sentiments were negative. CONCLUSION: Concerns over excessive and inappropriate digital screen use among Canadian children and adolescents have escalated recently. This work highlights that while there is much recent activity across sectors occurring in Canada, policy is lacking, and efforts lack coordination, cross-sectoral collaboration, and synergy, likely resulting in suboptimal impact.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.030 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".