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Record W3193666825 · doi:10.3390/socsci10080311

The Impact of Coparenting on Mothers’ COVID-19-Related Stressors

2021· article· en· W3193666825 on OpenAlexaffabout
Marsha Kline Pruett, Jonathan Alschech, Michael Saini

Bibliographic record

VenueSocial Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of TorontoUniversity of Northern British Columbia
Fundersnot available
KeywordsCoparentingStressorPsychologyDevelopmental psychologyCoronavirus disease 2019 (COVID-19)Test (biology)Clinical psychologyMedicine

Abstract

fetched live from OpenAlex

To test and explore whether more positive coparenting will significantly predict lower COVID-19-related stress across family configurations and dynamics and across both higher- and lower-income mothers, we developed and circulated an online survey among mothers from the U.S. and Canada. Coparenting was measured using the Coparenting Across Family Structures (CoPAFS) short form (27 items) scale, comprised of factors representing five coparenting dimensions: communication, respect, trust, animosity, and valuing the other parent. Items specific to COVID-19 stressors assessed the types of stressors each parent faced. The sample consisted of 236 North American mothers, mostly white (n = 187, 79.2%) and aged 30–50 years. The surveyed mothers reported a consistent and significant relation between more positive coparenting and less COVID-19-related stressors whether parents were living together or not, married or divorced, and with a lower or higher income level, suggesting the importance and centrality of positive coparenting as a key factor for family well-being. Coparenting was especially predictive among mothers who were never married and those with lower incomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
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 head0.043
GPT teacher head0.400
Teacher spread0.357 · 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.

Study designTheoretical or conceptual
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

Citations9
Published2021
Admission routes2
Has abstractyes

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