Bibliographic record
Abstract
Introduction: There is an increasing appreciation for the impact of socio-economic disadvantage on maternal health outcomes.This systematic review aimed to summarise the evidence for severe maternal morbidity (SMM) and maternal mortality (MM) in women who are socio-economically disadvantaged compared to those who are not, in highincome countries.Methods: A comprehensive search was conducted in MEDLINE, EMBASE, CINAHL, and PsycInfo databases.Peer-reviewed papers from observational studies were included.A narrative synthesis and meta-analyses of comparable studies, structured around the different definitions of socio-economic disadvantage and type of outcome (SMM or MM) were undertaken.Risk of bias was assessed using a modified Newcastle-Ottawa tool.Results: The final review included 49 papers; 27 cohort studies, 10 case-control and four cross-sectional and eight National Maternal Mortality Surveillance Programs.30 papers reported SMM and 22 MM, as the outcome.In the meta-analyses, in the most compared to the least amount of neighbourhood deprivation, neighbourhood income, neighbourhood poverty and low education, the odds of SMM were 1.45 (95% CI 1.13-1.85),1.44 (95% CI 1.32-1.57),1.61 (95% CI 0.97-2.66)and 1.33 (95% CI 1.19-1.49)respectively.In the most compared to the least amount of unemployment, neighbourhood deprivation, lowest occupational group and low education the odds of MM were 1.86 (95% CI 0.95-3.66),2.10 (95% CI 1.57-2.81),1.61 (95% CI 1.03-2.51),1.90 (95% CI 1.29-2.79)respectively.Discussion: Across high-income countries there is a consistent association between socio-economic disadvantage and SMM and MM.Further research is needed to identify targeted interventions to reduce increased risk.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.864 | 0.633 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".