Maternal COVID-19 Distress and Chinese Preschool Children’s Problematic Media Use: A Moderated Serial Mediation Model
Bibliographic record
Abstract
Introduction: Maternal distress increased during the COVID-19 pandemic, significantly impacting children's media use. The purpose of this study was to explore the influence mechanism of maternal COVID-19 distress on preschoolers' problematic media us through a moderated mediation model; specifically, we examined the possible mediating roles of parenting stress and negative instrumental use of media in parenting and the moderating role of supportive co-parenting. Methods: = 1.06; 47.4% boys) and their parents from six public kindergartens in Shanghai, China. The mothers provided information by completing measures on their levels of distress related to COVID-19, parenting stress levels, digital parenting practices, and perception of supportive co-parenting from their partners. Additionally, both parents rated their children's problematic media use. Results: (1) maternal COVID-19 distress was significantly and positively related to children's problematic media use; (2) this relationship was sequentially mediated by parenting stress and parents' negative instrumental use of media in parenting; and (3) supportive co-parenting moderated the serial mediation path by reducing the effect of maternal COVID-19 distress on parenting stress. Conclusion: The findings provide some support and guidance for preventing children's problematic media use and enhancing parental adaptation during the COVID-19 pandemic or in potentially adverse situations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".