A Scoping Review of the Health of Conflict-Induced Internally Displaced Women in Africa
Bibliographic record
Abstract
Armed conflict and internal displacement of persons create new health challenges for women in Africa. To outline the research literature on this population, we conducted a review of studies exploring the health of internally displaced persons (IDP) women in Africa. In collaboration with a health research librarian and a review team, a search strategy was designed that identified 31 primary research studies with relevant evidence. Studies on the health of displaced women have been conducted in South- Central Africa, including Democratic Republic of Congo (DRC); and in Eastern, East central Africa, and Western Africa, including Eritrea, Uganda, and Sudan, Côte d'Ivoire, and Nigeria. We identified violence, mental health, sexual and reproductive health, and malaria and as key health areas to explore, and observed that socioeconomic power shifts play a crucial role in predisposing women to challenges in all four categories. Access to reproductive health services was influenced by knowledge, geographical proximity to health services, spousal consent, and affordability of care. As well, numerous factors affect the mental health of internally displaced women in Africa: excessive care-giving responsibilities, lack of financial and family support to help them cope, sustained experiences of violence, psychological distress, family dysfunction, and men's chronic alcoholism. National and regional governments must recommit to institutional restructuring and improved funding allocation to culturally appropriate health interventions for displaced women.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".