Key Findings from 25 Years of IWPR Research Health, Safety, Violence, and Disaster: How Economic Analysis Improves Outcomes for Women and Families
Bibliographic record
Abstract
IWPR’s women’s health and safety efforts highlight the social and economic aspects of health, safety, and security issues. Over the past quarter century, the Institute has addressed women’s access to health insurance, the costs and benefits of preventive health services, reproductive health and rights, including the economic benefits of reproductive freedom, and the link between women’s socioeconomic status and health. IWPR’s examinations of safety issues have drawn attention to domestic violence as well as the effects of terrorism and disasters on women’s well-being. Its research has informed policy decisions by identifying both the limitations on access to health care services and ways to expand access, as well as the gender and racial/ethnic disparities in health outcomes. The Institute’s reports and resources have addressed a range of policy issues such as access to paid sick days including analyses of the health benefits of providing paid sickdays, breastfeeding protections under the Affordable Care Act, and in-home services for the elderly and others who need long-term care. For example, IWPR’s fact sheets and briefing papers include a 1994 analysis of the proposed Clinton health care reform’s access to health insurance for women of color, a policy update on abortion since the passage of Roe v. Wade, published in 2003, and an estimate in 2012 of potential benefits and cost savings, focused on savings from reduced emergency room use, anticipated with the adoption of mandatory paid sick days in New York City.
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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 | 0.036 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".