Birth Justice Philly: Equitable Community Engagement in Action
Bibliographic record
Abstract
This article describes how a multi-sector coalition focusing on carrying out recommendations of the Philadelphia Maternal Mortality Review Committee (MMRC) engages community participation through the development of equitable strategies that culminate in the implementation of actionable methods to improve perinatal outcomes.\nThe U.S. maternal mortality crisis continues to impact countless families and communities. Despite having some of the finest academic medical centers in the nation and a functioning county-level maternal mortality review committee, the maternal mortality rate in Philadelphia, Pennsylvania is far above the national average. Vital statistics show that Philadelphia’s rate of pregnancy-related deaths from 2012 to 2018 was 20 per 100,000 live births (Mehta et al., 2020), which is higher than the 2018 national rate of 17.4 per 100,000 live births (Lu et al., 2018). Philadelphia is the poorest of the nation’s 10 largest cities with more than a quarter of its 1.58 million people living in poverty. Racial inequities, substance use, and cardiovascular conditions have been identified as having a significant impact on higher death rates among pregnant and parenting people.\nTo efficiently address the recommendations that come from the Philadelphia MMRC, the Philadelphia Department of Public Health formed an action team. Organizing Voices for Action (OVA) is comprised of a multidisciplinary group of local stakeholders, including lived experience experts. Centering community voices and maintaining equitable practices have been embedded in the formation of the coalition by drafting an equity plan, with an equity statement and quarterly audits for accountability. Prioritizing stakeholder and community engagement foster collaboration in addressing root causes of maternal mortality.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.043 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".