The association of school connectedness and bullyinginvolvement with multiple screen-time behaviours amongyouth in two Canadian provinces: a COMPASS study
Bibliographic record
Abstract
INTRODUCTION: Screen time, a proxy for sedentary behaviours, has emerged as a critical health determinant among youth in contemporary societies, where most aspects of youth life involve access to screen-time devices. An understudied approach to reducing screen time among youth is bullying reduction. This study aims to understand the association between bullying perpetration, victimization, youth perception of the school environment and multiple screen-time behaviours. METHODS: A total of 44,861 youth aged between 13 and 18 years in two Canadian provinces completed a validated questionnaire that collected student data on health behaviours and outcomes, including multiple screen-time behaviours, bullying perpetration and victimization, and school connectedness. The outcome variables were total screen time, time spent watching television, playing video games, internet surfing, and communication- based screen-time behaviours. Using a random intercept, the final models were built using PROC MIXED in SAS 9.4. These models were adjusted for age, ethnicity, weekly disposable income, daylight hours, and weather variables. RESULTS: Compared to youth who reported non-involvement in bullying, youth who were bullies, victims, or both bullies and victims spent on average more minutes per day in front of screens across all screen time categories. Youth who felt happy and safe at school, and who perceived their teachers as being fair, reported lower levels of multiple screen-time behaviours. CONCLUSION: With non-involvement in bullying showing a strong negative association with multiple screen-time behaviours, school policies to address bullying and screen time through school connectedness could offer a novel approach in minimizing these harmful behaviours.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".