The post-2015 agenda: staying the course in maternal and child survival
Bibliographic record
Abstract
In this article, we draw on available evidence from Countdown to 2015 and other sources to make the case for keeping women and children at the heart of the next development agenda that will replace the Millennium Development Goal (MDG) framework after 2015. We provide a status update on global progress in achieving MDGs 4 and 5, reduce child mortality and improve maternal health, respectively--showing that although considerable mortality reductions have been achieved, many more women's and children's lives can be saved every day through available, cost effective interventions. We describe key underlying determinants of poor maternal and child health outcomes and the need for well-coordinated, comprehensive approaches for addressing them such as introducing a combination of nutrition specific and sensitive interventions to reduce pervasive malnutrition, targeting interventions to the underserved to reduce inequities in access to care, and increasing women's social status through improved access to education and income-earning opportunities. In the wake of population momentum and emergencies such as the recent ebola outbreak and other humanitarian crises, health systems must be strengthened to be able to respond to these pressures. In conclusion, we underscore that the unfinished business of women's and children's health must be prioritized in the days ahead, and that ending preventable maternal and child deaths is not only a moral obligation but is achievable and essential to sustainable development moving forward.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".