Advancing sexual and reproductive health and rights in low- and middle-income countries: Implications for the post-2015 global development agenda
Bibliographic record
Abstract
The papers and commentaries in this special issue illuminate progress made by low- and middle-income countries towards implementation of the Programme of Action (PoA) agreed by 179 countries during the International Conference on Population and Development in Cairo in 1994. The PoA presents a path-breaking sexual and reproductive health and rights (SRHR) framework for global and national population and health policies. While progress towards implementation has been made at global, regional and national levels, continuing and new challenges require that high priority be given to SRHR for all, particularly women and girls, during the remaining months of the millennium development goals and in the United Nations post-2015 development agenda. This paper highlights three critical gaps, raised in other papers: inequalities in access to sexual and reproductive health (SRH) information and services; the widespread need to improve SRH services to meet public health, human rights and medical ethics standards for quality of care; and the absence or inadequate use of accountability mechanisms to track and remedy the other two. We discuss priority actions to achieve equality, quality and accountability in SRHR policies, programmes and services, especially those that should be included in the post-2015 development agenda.
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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 | 0.048 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 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".