Parental factors associated with screen time in pre-school children in primary-care practice: a TARGet Kids! study
Bibliographic record
Abstract
OBJECTIVE: To identify child and parental factors associated with screen time in 3-year-old children. DESIGN: Observational study. SETTING: Participants were recruited from a large primary-care paediatric group practice in Toronto, Canada. SUBJECTS: Healthy 3-year-old children were included. A questionnaire was completed by their parents on screen time. Descriptive statistics and linear regression models were used to assess associations between child screen time and selected factors. Multivariable models included factors from the univariate analysis with P < 0·1. Estimated effects and 95% CI are reported. RESULTS: A total of 157 children were enrolled (91% recruitment). The mean screen time per weekday was 104 min (similar for weekend day). In all, 10% of children had a television (TV) in their bedroom; 59% consumed at least one meal while watching TV; and 81% of parents had household rules about screen time. Controlling for maternal education and age, eating lunch and dinner in front of the screen and mother being employed were associated with an increase in child weekday screen time of 96 (95% CI 30, 192), 42 (95% CI 12, 90) and 36 (95% CI 6, 72) min/d, respectively. Eating lunch in front of the screen and an increase of 1 h of parental screen time were associated with an increase of 78 (95% CI 36, 132) and 12 (95% CI 6, 18) min/d in child weekend screen time. Family rules decreased child weekend screen time by 30 (95% CI 6, 54) min/d. CONCLUSIONS: Interventions that include these important parental factors should be evaluated for their effectiveness in reducing 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".