Improving the Response of Health Systems to Female Genital Schistosomiasis in Endemic Countries through a Gender-Sensitive Human Rights-Based Framework
Bibliographic record
Abstract
The right to health was enshrined in the constitution of the World Health Organization in 1946 and in the Universal Declaration of Human Rights in 1948, which also guaranteed women's fundamental freedoms and dignity. The Declaration of Human Rights was signed by almost every country in the world. Nonetheless, gender inequalities in health and health systems continue to persist, especially in lower and middle income countries that are disproportionately affected by a litany of neglected diseases. In this paper, we focus on one of the most neglected human rights, development, and reproductive health issues globally, female genital schistosomiasis (FGS), which imposes enormous unacknowledged suffering on an estimated 56 million women and girls in Sub-Saharan Africa. Despite increasing calls for attention to FGS, no country has fully incorporated it into its health system. An appropriate response will require a comprehensive approach, guided by human rights mandates and the redress of FGS-related gender inequalities. In this paper, we propose the application of existing human rights and its clients, women, and girls affected by FGS as rights holders. Within the different components or building blocks of the health system, we propose elements of an appropriate health system response using the four components identified within the FGS Accelerated Scale Together (FAST) Package-awareness raising, prevention of infection, training of health personnel, and diagnosis and treatment. The framework is aspirational, its recommended elements and actions are not exhaustive, and countries will need to adapt it to their own situations and resource availability. However, it can be a useful guide to help health systems define how to begin to incorporate FGS into their programming in a way that responds to their human rights obligations in a gender- and culturally sensitive manner.
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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 | 0.046 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".