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Record W4315481970 · doi:10.1177/23780231221145067

Trends in the Parenthood Gap in Health and Well-Being among U.S. Women from 1996 to 2018

2023· article· en· W4315481970 on OpenAlexaff
Kei Nomaguchi, Melissa A. Milkie

Bibliographic record

VenueSocius Sociological Research for a Dynamic World · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHappinessPsychologyDepression (economics)PandemicWell-beingDevelopmental psychologyDemographyCoronavirus disease 2019 (COVID-19)DiseaseMedicineSocial psychologySociologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The notion that U.S. mothers with minor children are less happy and more depressed than nonmothers largely relies on data collected in the 1990s or earlier. Although the coronavirus disease 2019 pandemic brought much attention to the stressfulness of parenting, we lack knowledge of how mothers fared relative to nonmothers in the 2000s and 2010s, before the pandemic. The authors investigate trends in the parenthood gap in happiness, depression, and self-rated health among women aged 18 to 59 years, using the 1996 to 2018 General Social Survey ( n = 13,254) and the 1997 to 2018 National Health Interview Survey ( n = 263,110). Results indicate that twenty-first-century mothers with younger children were better off than nonmothers on two measures, reporting less depression and better health. Mothers’ “depression advantage” grew across this time. However, mothers with older children reported less happiness than nonmothers, a continued trend from the 1990s. The study underscores the importance of examining various well-being indicators across the changing contexts of parenting.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.441
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSocius Sociological Research for a Dynamic WorldSame topicIntergenerational Family Dynamics and CaregivingFrench-language works237,207