The role of government assistance, housing, and employment on postpartum maternal health across income and race: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Stressful large-scale events, such as the COVID-19 pandemic and natural disasters, impact birthing individuals' postpartum experiences and their mental health. Resultant changes in government assistance, housing, and employment may further exacerbate these impacts, with differences experienced by varying income levels and races. This study aimed to examine maternal depression and anxiety in postpartum individuals by income and race during a stressful large-scale event, and the mediating role of government assistance, housing, and employment. METHODS: An explanatory sequential mixed methods study was conducted (QUANT + QUAL). For aim 1 (quantitative), birthing individuals who delivered during peak pandemic (June 2020 - September 2021) completed questionnaires related to their perinatal experiences and mental health. Macrosystem factors (government assistance, housing, and employment changes) were assessed using the Psychosocial Recommended Measures. The Edinburgh Postnatal Depression Scale (EPDS) and the Generalized Anxiety Disorder-7 (GAD7) assessed depression and anxiety, respectively. Serial linear regression models assessed the relationship between race and income with mental health and macrosystem factors. For aim 2 (qualitative), 40 individuals from the quantitative study balanced by income (low vs. high income) and race (Black vs. White) completed one-on-one semi-structured interviews which were analyzed using thematic analysis. RESULTS: Amongst 1582 birthing individuals, Black individuals had a significantly higher EPDS score compared to White counterparts. Not receiving government assistance, unstable housing, and experiencing various employment changes were all related to worse mental health during stressful large-scale events. In semi-structured interviews, low-income individuals discussed that government assistance helped alleviate a financial and mental burden. Low- and high-income individuals reported varying job changes that impacted their mental health (low-income: job loss, high-income: increased hours). CONCLUSIONS: This research spotlights the negative impact of large-scale events most affected both Black and low-income individuals' postpartum mental health, and the role of government assistance, stable housing, and secure employment in helping to alleviate these disparities between income levels.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".