The Emergent Motherhood Mental Health Advantage: Did Pandemic Times Make a Difference?
Bibliographic record
Abstract
Research indicates that a new pattern of motherhood well-being advantage emerged in the 2010s for U.S. women. Although scholars have argued that maternal mental health worsened during the COVID-19 pandemic, whether the parenthood mental health gap changed during the pandemic is unclear. Using data from the National Health Interview Survey (N = 29,241), this study examines the parenthood gap in yearly and quarterly changes in anxiety and depression during 2019-2021 for women aged 18-59, with attention to variation by partnership status. The results show that changes in anxiety and depression prevalence were similar across parental and partnership statuses, with indications that maternal advantages expanded among women who were single. In October-December 2020, anxiety prevalence increased more for single women without minor children of their own living in the household ("nonmothers") than for single or partnered mothers. In April-June 2021, anxiety declined among mothers, especially single mothers, but remained higher than before the pandemic among single nonmothers. Some of these group differences in anxiety changes became nonsignificant after we controlled for household economic conditions, which were better in 2021 than in 2019 for all groups, particularly single mothers. In sum, trends in motherhood mental health advantages continued throughout the pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".