Project Title: Home Birthing: Analyzing the Value of Home Births to American Mothers
Bibliographic record
Abstract
There were about 3.7 million births in the United States in 2018, and a maternal mortality rate of 17.4 deaths per 100,000 live births (37.3 for non-Hispanic black women). The US also has high rates of birth interventions such as episiotomy and instrumental vaginal births, and increasing rates of cesarean sections, and while these interventions can be life saving, they do not always result in better birth outcomes. When the high US maternal mortality rates are compared to those of peer countries such as Canada and the UK, the data suggests that there is much room for improvement in how the US healthcare system regulates birth. My experience studying reproduction practices globally exposed me to the diverse ways other countries approach reproductive and prenatal care, particularly more frequent use of midwives as birth attendants and home as an acceptable setting. Though this is a complex issue, this course made me question the necessity of frequent birth interventions. This paper explores how a more widespread adoption of homebirth and midwifery practices, moving towards a more normal physiological birthing process, could improve birth outcomes. The research was conducted through an in-depth examination of the current literature around this subject and an interview with an experienced nurse midwife who attends home births. It was evaluated to see how a more hands-off birth approach could improve both birth outcomes such as maternal mortality and women’s experiences during labor. The results suggest that women are served well physically and mentally when they are allowed to have more control during labor. Further research to gain both qualitative and quantitative data around and from women in the US who chose to give birth at home, and its value to mothers’ health is needed as this becomes a more popular option.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".