Women's Rights and Women's Health During HIV/AIDS Epidemics: The Experience of Women in Sub-Saharan Africa
Bibliographic record
Abstract
Twenty-five years have passed since HIV/AIDS was recognized as a major public health problem. Although billions of dollars are spent in research and development, we still have no medical cure or vaccination. In the early days of the epidemic, public health slogans suggested that HIV/AIDS does not discriminate. Now it is becoming clear that HIV/AIDS spreads most rapidly among poor, marginalized, women, colonized, and disempowered groups of people more than others. The HIV/AIDS epidemic is exacerbated by the social, economic, political, and cultural conditions of societies such as gender, racial, class, and other forms of inequalities. Sub-Saharan African countries are severely hit by HIV/AIDS. For these countries the pandemic of HIV/AIDS demands the need to travel extra miles. My objective in this article is to promote the need to go beyond the biomedical model of "technical fixes" and the traditional public health education tools, and come up with innovative ideas and strategic thinking to contain the epidemic. In this article, I argue that containing the HIV/AIDS epidemic and improving family and community health requires giving appropriate attention to the social illnesses that are responsible for exacerbating biological disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".