Chinese Parental Mediation, Predictors, and Associations with Children’s Problematic Media Use: A Latent Profile Analysis
Bibliographic record
Abstract
Research Findings: This study aims to examine the latent profiles of parents’ mediation and their predictors, as well as links between different profiles and children’s problematic media use. A total of 1415 children aged 3–6 years (47.8% boys) and their paired parents were recruited in Shanghai, China and surveyed demographic information, parents’ mediation practice and marital conflict, and children’s media use problems. Latent profile analyses, tests of variance, and logistic regression analyses were used for data analysis. The results indicated that: (1) four potential profiles of mediation were yielded: mother-dominated mediation, father-dominated mediation, coordinated high-level mediation, and coordinated low-level mediation; (2) there were significant associations between children’s age, fathers’ age, parents’ educational backgrounds and marital conflict with mediation profiles; and (3) the likelihood of children experiencing problematic media use was lowest in the consistent high-level group, followed by the mother-dominated and the father-dominated group, and highest in the consistent low-level group. Practice or Policy: These findings imply that parents’ digital parenting patterns are influenced by multiple factors, and that parents who are older, have less education, have older children, and experience more marital conflict should be given more support and assistance to improve parents’ child-rearing and child development.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".