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Record W4400553268 · doi:10.1186/s12889-024-19382-0

Maternal distress, parenting stress, maladaptive parenting and children’s problematic media use in China: evidence from 2022 spring in Shanghai

2024· article· en· W4400553268 on OpenAlexaff
Jingyao Wang, Shumin Wang, Bowen Xiao, Juan Li, Yuemin Feng, Yan Li

Bibliographic record

VenueBMC Public Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsCarleton University
Fundersnot available
KeywordsMediationMental healthDistressBiostatisticsScreen timeMedicineClinical psychologyDevelopmental psychologyPublic healthPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract The COVID-19 lockdown has forced young children to spend more time on media and significantly impacted their mothers’ mental health. This study explored how mothers’ individual distress influences children’s problematic media use during the Shanghai citywide lockdown caused by COVID-19. Data were collected from 1889 Chinese mothers ( M age = 34.69 years, SD = 3.94 years) with preschoolers aged 3–6 years ( M ag e = 4.38 years, SD = 1.06 years; 49.0% boys) via an online survey. The statistical analyses relied on SPSS Statistics version 26.0 and macro-program PROCESS 3.3. to investigate the associations and mediation analysis among all the study variables. The results indicated a positive association between maternal distress and children’s problematic media use, mediated by parenting stress and maladaptive parenting. Specifically, the serial mediation analysis revealed that high levels of maternal distress exacerbate parenting stress, which in turn leads to maladaptive parenting practices. These maladaptive practices subsequently increase problematic media use in preschool children. The findings highlighted that parents need to enhance their ability to manage risk and promote mental health during periods of significant stress and routine disruption to reduce children’s problematic media use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.309
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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