Assessing Transportation Barriers to Maternal Care for Black Women in Los Angeles County
Bibliographic record
Abstract
The United States ranks among the worst high-income countries for maternal health outcomes, with Black women experiencing disproportionately high and alarming rates of maternal mortality and morbidity. In Los Angeles County, Black women are four times more likely to die from pregnancy-related causes than women of other racial and ethnic groups. These disparities may partially be attributed to social determinants of health, including transportation access. Lack of transportation can hinder access to healthcare, with significant consequences for reproductive health. This study investigates how transportation barriers affect Black birthing people's access to maternal healthcare in Los Angeles. In partnership with Black Women for Wellness, we conducted a descriptive, observational study using an online survey completed by 235 respondents, all of whom self-identified as women. Findings reveal that Black women in Los Angeles face substantial transportation barriers when seeking maternal healthcare, including limited public transportation, lack of personal vehicles, and challenges in securing rides. Many participants reported that these issues caused delayed or missed prenatal appointments. These results underscore the urgent need for policy interventions and systems-level solutions to improve transportation access. Addressing these barriers is essential for reducing maternal health disparities and improving outcomes for Black women.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".