The Integration of Doulas into the Pregnancy Care Teams of Women of Color in the United States
Bibliographic record
Abstract
The United States has one of the highest maternal mortality ratios of any country in the Organization for Economic Cooperation and Development (OECD) at 17.4 per 100,000 live births as of 2017, far more than Canada, Sweden, Australia, or Germany (OECD.Stat, 2021). A primary reason for this considerable difference between the U.S. and other OECD countries is the racial disparity in maternal mortality. One intervention aimed at addressing this racial disparity is the integration of a doula, a professionally trained companion who provides birth support, into a woman’s biomedical care team (Gilliland, 2002). The doula works with their client throughout pregnancy, through delivery, and in the postnatal period, providing guidance and encouragement to the mother while also working within the healthcare system to advocate for, and in some cases to protect, her. This paper will provide a general background on maternal death in the United States, highlighting the historical and modern instances of racism and its impact on maternal health; examining the use of doulas as an intervention for reducing maternal death, using New York City as a case study; and discussing the potential implications of a national expansion of Medicaid reimbursed doula programs aimed at improving maternal outcomes countrywide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".