Health Behaviors, Mental Health, and Health Care Utilization Among Single Mothers After Welfare Reforms in the 1990s
Bibliographic record
Abstract
We studied the health of low-income US women affected by the largest social policy change in recent US history: the 1996 welfare reforms. Using the Behavioral Risk Factor Surveillance System (1993-2012), we performed 2 types of analysis. First, we used difference-in-difference-in-differences analyses to estimate associations between welfare reforms and health outcomes among the most affected women (single mothers aged 18-64 years in 1997; n = 219,469) compared with less affected women (married mothers, single nonmothers, and married nonmothers of the same age range in 1997; n = 2,422,265). We also used a synthetic control approach in which we constructed a more ideal control group for single mothers by weighting outcomes among the less affected groups to match pre-reform outcomes among single mothers. In both specifications, the group most affected by welfare reforms (single mothers) experienced worse health outcomes than comparison groups less affected by the reforms. For example, the reforms were associated with at least a 4.0-percentage-point increase in binge drinking (95% confidence interval: 0.9, 7.0) and a 2.4-percentage-point decrease in the probability of being able to afford medical care (95% confidence interval: 0.1, 4.8) after controlling for age, educational level, and health care insurance status. Although the reforms were applauded for reducing welfare dependency, they may have adversely affected health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".