Patterns of parent screen use, child screen time, and child socioemotional problems at 5 years
Bibliographic record
Abstract
Background: Digital media screens have become an essential part of our family life. While most studies focus on children’s screen use, we know less about parental screen use patterns and how these affect children’s socio-emotional development.Method: 867 Canadian parents of 5-year old children from the TARGet Kids! Cohort (73.1% mothers, mean age=38.88±4.45 years) participated from 2014 to 2020. Parents reported parental and child time on TV and handheld devices and completed the Strengths and Difficulties Questionnaire (SDQ). Latent profile analysis (LPA) was used to identify groups of parents with similar patterns of screen use and link these profiles with child screen time and SDQ.Results: We identified six latent profiles of parent screen use: low users (P1, reference; n=323), more TV than handheld (P2; n=261), equal TV and handheld (P3; n=177), more handheld than TV (P4; n=57), high TV and handheld (P5; n=38) and extremely high TV and handheld (P6; n=11). P6 were more likely to be living in single parent households compared to P1 (estimate=-1.49(0.70), p=0.03). P2 (estimate=-0.67(0.32), p=0.04) and P4 (estimate=-1.42(0.40), p<0.001) were more likely to have lower household income compared to P1. P4 (χ2=12.32, p<0.001) and P5 (χ2=9.54, p=0.002) have higher total child screen time compared to P1. P6 (χ2=6.82, p=0.009) had higher total SDQ compared to P1.Discussion: Patterns of parent screen use were associated with child screen use and child socioemotional problems. The link between parental screen use profiles and child behaviours suggests a need for guidelines on parental screen time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".