Parental Influence on Children’s Media Use in South Korea: National Population-Based Study
Bibliographic record
Abstract
Background: To better understand the effects of media use on children, it is essential to examine the various factors influencing the media use of digital native children. In the situational context, parental media usage, parents' attitudes toward media, and parenting styles have all been identified as significant factors influencing children's media use. This study focuses on the key factors and examines these relationships in greater depth, drawing on existing research to understand their impact on the media usage patterns of digital native children. Objective: This study examines parental influences related to young children's media use in Korea over a 3-year period (2022-2024) using independent, nationally representative cohorts. Methods: Using multigroup structural equation modeling, we analyzed data from 3 independent parent-reported cohorts (for 2022, n=1058; for 2023, n=1020; for 2024, n=1020) to investigate how parental media habits, attitudes, and distinct parenting styles predict children's daytime and nighttime media consumption. Results: The online survey results revealed that parental media time, particularly for mothers, consistently correlated with higher levels of children's daytime media use (β=.002-.003). Positive parental attitudes toward media increased children's daytime media use (β=.028-.102), whereas negative attitudes had a limited effect (β=-.069-.140). Among the 7 parenting styles, positive parenting consistently reduced children's daytime media use in 2022 and 2023 (β=-.228 for 2022, β=-.215 for 2023), but harsh punishment emerged as the strongest factor in daytime media use in 2024 (β=-.078 for 2022, β=-.090 for 2023, and β=-.072 for 2024). Notably, parenting styles showed no significant effect on children's nighttime media use throughout the study, suggesting that parental influence may be more effective during daytime hours. Conclusions: This analysis extends existing research by differentiating media use patterns across time periods and highlights the evolving influence of parenting styles. These findings have implications for the development of targeted parental guidelines for managing young children's media exposure, especially as digital media continues to become a pervasive part of daily life.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".