Demographic, parental and home environment correlates of traditional and mobile screen time in preschool‐aged children
Bibliographic record
Abstract
OBJECTIVES: Research on the correlates of screen time in young children, that could be targeted in future interventions to improve healthy development, has primarily focused on TV viewing with little consideration of mobile devices. The objectives of this study were to examine the associations between a range of demographic, parental, and home environment correlates and preschool-aged children's TV/video viewing, video/computer game playing, and total screen time across traditional and mobile devices. METHODS: The results of this cross-sectional study are based on 106 preschool-aged children (3-5 years) and their parents recruited in 2018 in Edmonton, Alberta. Children's and parental demographic information, home characteristics, and information about parental and children's screen time use was measured using a parent questionnaire. Simple and multiple linear regression models were conducted. RESULTS: Each additional hour/day of parental screen time was associated with 12 (95%CI = 5.2, 19.8) minutes/day of children's TV/video, 6 (95%CI = 1.5, 11.0), minutes/day of video/computer game playing, and 19 (95%CI = 8.9, 29.2) minutes/day of total screen time. Additionally, significant associations of technology interference and presence of electronics in the bedroom with children's screen time were attenuated in the multiple regression models. CONCLUSIONS: Parental screen time appears important to target in future family-based screen time interventions and initiatives. Future studies should explore potential mediating or moderating variables between parental screen time and children's screen time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".