A proposed new international convention supporting the rights of pregnant women and girls and their newborn infants
Bibliographic record
Abstract
For a multitude of eminently modifiable reasons, death rates for pregnant women and girls and their newborn infants in poorly resourced countries remain unacceptably high. The concomitant high morbidity rates compound the situation. The rights of these vulnerable individuals are incompletely protected by existing United Nations human rights conventions, which many countries have failed to implement. The authors propose a novel approach grounded on both human rights and robust evidence-based clinical guidelines to create a 'human rights convention specifically for pregnant women and girls and their newborn infants'. The approach targets the 'right to health' of these large, vulnerable and neglected populations. The proposed convention is designed so that it can be monitored, audited and evaluated objectively. It should also foster a sense of national ownership and accountability as it is designed to be relevant to local situations and to be incorporated into local clinical governance systems. It may be of particular value to those countries that are not yet on target to meet the Millennium Development Goals (MDGs), especially MDGs 4 and 5, which target child and maternal mortality, respectively. To foster a sense of international responsibility, two additional initiatives are integral to its philosophy: the promotion of twinning between well and poorly resourced regions and a raising of awareness of how some well-resourced countries can damage the health of mothers and babies, for example, through the recruitment of health workers trained by national governments and taken from the public health system.
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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 | 0.049 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.027 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".