Widespread rape does not directly appear to increase the overall HIV prevalence in conflict-affected countries: so now what?
Bibliographic record
Abstract
BACKGROUND: Sub-Saharan Africa (SSA) is severely affected by HIV/AIDS and conflict. Sexual violence as a weapon of war has been associated with concerns about heightened HIV incidence among women. Widespread rape by combatants has been documented in Burundi, Sierra Leone, Rwanda, Democratic Republic of Congo, Liberia, Sudan and Uganda. To examine the assertion that widespread rape may not directly increase HIV prevalence at the population level, we built a model to determine the potential impact of varying scenarios of widespread rape on HIV prevalence in the above seven African countries. DISCUSSION: Our findings show that even in the most extreme situations, where 15% of the female population was raped, where HIV prevalence among assailants was 8 times the country population prevalence, and where the HIV transmission rate was highest at 4 times the average high rate, widespread rape increased the absolute HIV prevalence of these countries by only 0.023%. These projections support the finding that widespread rape in conflict-affected countries in SSA has not incurred a major direct population-level change in HIV prevalence. However, this must not be interpreted to say that widespread rape does not pose serious problems to women's acquisition of HIV on an individual basis or in specific settings. Furthermore, direct and indirect consequences of sexual violence, such as physical and psychosocial trauma, unwanted pregnancies, and stigma and discrimination cannot be understated. SUMMARY: The conclusions of this article do not significantly change current practices in the field from an operational perspective. Proper care and treatment must be provided to every survivor of rape regardless of the epidemiological effects of HIV transmission at the population level. Sexual violence must be treated as a protection issue and not solely a reproductive health and psychosocial issue. It is worth publishing data and conclusions that could be misconstrued and may not make much of a programmatic difference in the field. Data, if collected, analysed and interpreted carefully, help to improve our understanding of complicated and nuanced situations. Ultimately, our understanding of what the outcomes of such interventions can achieve will be more realistic. It also helps decision-makers prioritise their funding and interventions.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".