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Record W4284669150 · doi:10.3389/fpsyg.2022.905226

Parenting in a 24/7 Economy: Mothers’ Non-standard Work Schedules and Involvement in Children’s Education

2022· article· en· W4284669150 on OpenAlexaff
Minseop Kim, Nahri Jung, Larasati Wulandari

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversity of Toronto
FundersResearch Grants Council, University Grants Committee
KeywordsWork (physics)PsychologyAffect (linguistics)Work scheduleBalance (ability)Developmental psychologyEarly childhood educationWork hoursWarrantEconomics

Abstract

fetched live from OpenAlex

Evidence suggests that parental involvement in children’s education has a positive impact on children’s educational achievements and wellbeing outcomes. The trend toward 24/7 economies has obliged many mothers to work non-standard schedules (i.e., schedules outside traditional daytime, Monday–Friday work schedules). This has raised concerns over how non-standard work schedules affect parenting behaviors, including paternal involvement in children’s education. Using data from mothers of young children (age 5–6) in Hong Kong ( N = 433), this study examined the association between maternal work schedules and involvement in children’s home and school activities. The results of structural equation modeling found that weekend work was associated with lower levels of home-based involvement. By contrast, working non-standard hours was linked with higher levels of school-based involvement. This pattern suggested that non-standard work schedules could make it either easier or harder for mothers to balance work and family responsibilities, depending on the circumstances (i.e., whether they work non-standard hours or weekends). These findings, indicating that the effects of non-standard work schedules are not uniformly negative or positive, warrant sophisticated policy efforts to address the potential adverse effects of non-standard work schedules and avoid undermining their potential to be used as a family-friendly option.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.112
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.328
Teacher spread0.306 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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