Is the Association Between Early Childhood Screen Media Use and Effortful Control Bidirectional? A Prospective Study During the COVID-19 Pandemic
Bibliographic record
Abstract
Individual differences in effortful control, a component of temperament, reflecting the ability to use attention and other cognitive processes to self-regulate emotion and behavior, contribute to child academic adjustment, social competence, and wellbeing. Research has linked excessive screen time in early childhood to reduced self-regulation ability. Furthermore, research suggests that parents are more likely to use screens with children who have more challenging temperaments, such as low levels of effortful control. Since screen time by children between the ages of 0 and 18 has increased during the COVID-19 pandemic, it remains timely to investigate the developmental pattern of association between child screen media use and effortful control. We hypothesize that higher levels of screen media intake at age 3.5 will be associated with lower effortful control at age 4.5 and that lower effortful control at 3.5 will contribute to more screen media intake at age 4.5. This study draws on participants followed longitudinally over the span of 2-years for an investigation of Canadian preschoolers’ screen media use during the pandemic ( N = 316, Wave 1). A follow-up with this sample was completed in 2021 ( N = 265, Wave 2). Analyses using a cross-lagged panel model revealed stability in child screen time and effortful control between the ages of 3.5 and 4.5. Child screen time at age 3.5 significantly contributed to decreased effortful control scores at the age of 4.5, whereas effortful control at age 3.5 did not contribute to screen time at age 4.5. Our results partially confirmed our hypothesis and indicated that higher levels of screen time intake were detrimental to the development of effortful control. These results suggest that screen media use, an exceedingly frequent activity, may play an enduring role in development by shaping young children’s temperaments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".