Barriers to safe motherhood in India.
Bibliographic record
Abstract
Maternal mortality remains unacceptably high in India even though this hard-to-measure indicator has likely recently started to decline. For 2005–2006 mortality ratios range from the Indian government’s estimate of 301 maternal deaths per 100000 live births to the World Health Organization’s estimate of 450. The government’s state-level estimates range from 517 maternal deaths per 100000 live births in the most populous state Uttar Pradesh to 110 in the small state of Kerala. India contributes nearly one-quarter of the world’s maternal deaths so its insufficient progress in reducing maternal mortality imperils not only its own targets but also the global achievement of the Millennium Development Goal to reduce maternal mortality by 75% from 1990 levels by 2015. A recent decline in fertility (from 3.4 children per woman in 1993 to 2.7 children in 2006) has greatly helped to lower the number of Indian women dying from these causes and their lifetime risk of maternal death. Hemorrhage is the leading cause of maternal death in India; it is responsible for nearly two-fifths of all maternal deaths and thus accounts for half of the direct causes. Women’s receipt of any professional prenatal or delivery care has increased dramatically -- by one-half and one-third respectively from 1993 to 2006. Nonetheless just over half (52%) of all Indian women deliver without trained medical assistance. Nearly three-fourths of women still give birth with no medical professional in attendance in Uttar Pradesh and Bihar the country’s first and third most populated states respectively. Recently enacted programs to improve the safety of pregnancy and childbirth are likely behind the substantial increase in the proportion of women attended by trained professionals at delivery. Nevertheless if India is to achieve its goal of 100 maternal deaths per 100000 live births government at all levels must redouble efforts to improve access to information and services to protect women’s health during pregnancy and delivery and to prevent unintended pregnancy and unsafe abortion.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".