Global inequities in adverse pregnancy outcomes: what can we do?
Bibliographic record
Abstract
The Health Equity Leadership & Exchange Network states that "health equity exists when all people, regardless of race, sex, sexual orientation, disability, socioeconomic status, geographic location, or other societal constructs, have fair and just access, opportunity, and resources to achieve their highest potential for health." It is clear from the wide discrepancies in maternal and infant mortalities, by race, ethnicity, location, and social and economic status, that health equity has not been achieved in pregnancy care. Although the most obvious evidence of inequities is in low-resource settings, inequities also exist in high-resource settings. In this presentation, based on the Global Pregnancy Collaboration Workshop, which addressed this issue, the bases for the differences in outcomes were explored. Several different settings in which inequities exist in high- and low-resource settings were reviewed. Apparent causes include social drivers of health, such as low income, inadequate housing, suboptimal access to clean water, structural racism, and growing maternal healthcare deserts globally. In addition, a question is asked whether maternal health inequities will extend to and be partially due to current research practices. Our overview of inequities provides approaches to resolve these inequities, which are relevant to low- and high-resource settings. Based on the evidence, recommendations have been provided to increase health equity in pregnancy care. Unfortunately, some of these inequities are more amenable to resolution than others. Therefore, continued attention to these inequities and innovative thinking and research to seek solutions to these inequities are encouraged.
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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 | 0.043 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 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".