Advancing the sexual and reproductive health and human rights of women living with HIV
Bibliographic record
Abstract
INTRODUCTION: Many women living with HIV can have safe, healthy and satisfying sexual and reproductive health, but there is still a long way to go for this to be a reality, especially for the most vulnerable amongst them who face repeated violations of their rights. DISCUSSION: The contributions in this Supplement from researchers, clinicians, programme managers, policy makers, and women living with HIV demands an important appreciation that the field of sexual and reproductive health and human rights for women living with HIV is complex on many levels, and women living with HIV form a very diverse community. CONCLUSIONS: The manuscripts emphasize that attention must be paid to the following critical dimensions: 1) Placing human rights and gender equality at the centre of a comprehensive approach to health programming, in particular in relation to sexuality and sexual health; 2) Ensuring health systems responsiveness to minimizing inequalities in access to health care and quality of care that often do not meet the needs of women living with HIV; 3) Engaging and empowering women living with HIV in the development of policies and programmes that affect them; and 4) Strengthening monitoring, evaluation and accountability procedures to provide good quality data and ensuring remedies for violations of health and human rights of women living with HIV.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".