Maternal Depression in the United States: Nationally Representative Rates and Risks
Bibliographic record
Abstract
OBJECTIVES: To examine the public health burden of major depressive disorder (MDD) among mothers: its prevalence and sociodemographic patterns; associated functioning, comorbidities, and adversities; and racial/ethnic disparities. METHODS: This was a cross-sectional analysis of 8916 mothers in the National Epidemiologic Survey of Alcohol and Related Conditions, a nationally representative survey of the civilian U.S. population in 2001?2002. Past-year MDD was assessed with a structured interview protocol. RESULTS: Ten percent of mothers experienced depression in the past year. White and Native American women, those with low education or income, and those not married had high rates of depression. Depression was not strongly patterned by number of or age of children. Depressed mothers experienced more adversities (poverty, separation or divorce, unemployment, financial difficulties) and had worse functioning. Half of depressed mothers received services for their depression. Black and Hispanic depressed mothers were more likely to experience multiple adversities and less likely to receive services than white depressed mothers. CONCLUSIONS: Maternal depression is a major public health problem in the United States, with an estimated 1 in 10 children experiencing a depressed mother in any given year. Professionals who work with mothers and children should be aware of its prevalence and its detrimental effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".