The Use of Audit to Identify Maternal Mortality in Different Settings: Is It Just a Difference Between the Rich and the Poor?
Bibliographic record
Abstract
OBJECTIVE: To illustrate how maternal mortality audit identifies different causes of and contributing factors to maternal deaths in different settings in low- and high-income countries and how this can lead to local solutions in reducing maternal deaths. DESIGN: Descriptive study of maternal mortality from different settings and review of data on the history of reducing maternal mortality in what are now high-income countries. SETTINGS: Kalabo district in Zambia, Farafenni division in The Gambia, Onandjokwe district in Namibia, and The Netherlands. POPULATION: Population of rural areas in Zambia and The Gambia, peri-urban population in Namibia and nationwide data from The Netherlands. METHODS: Data from facility-based maternal mortality audits from three African hospitals and data from the latest confidential enquiry in The Netherlands. MAIN OUTCOME MEASURES: Maternal mortality ratio (MMR), causes (direct and indirect) and characteristics. RESULTS: MMR ranged from 10 per 100,000 (The Netherlands) to 1,540 per 100,000 (The Gambia). Differences in causes of deaths were characterized by HIV/AIDS in Namibia, sepsis and HIV/AIDS in Zambia, (pre-)eclampsia in The Netherlands and obstructed labour in The Gambia. CONCLUSION: Differences in maternal mortality are more than just differences between the rich and poor. Acknowledging the magnitude of maternal mortality and harnessing a strong political will to tackle the issues are important factors. However, there is no single, general solution to reduce maternal mortality, and identification of problems needs to be promoted through audit, both national and local.
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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 | 0.040 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".