Exploring Congolese refugees’ experiences with abortion care in Uganda: a multi-methods qualitative study
Bibliographic record
Abstract
Uganda hosts 1.4 million refugees and conflict-affected people. Widely regarded as the best place in Africa to be a refugee, Uganda's policies encourage self-sufficiency and local integration. However, abortion is legally restricted and recent studies suggest that displaced women and girls have persistent unmet sexual and reproductive health needs. In 2017, we conducted a multi-methods study to assess the reproductive health needs of displaced Congolese women in camp- and urban-based settings in Uganda. Our project focused on maternal health and delivery care, contraception, and abortion/post-abortion services and the intersection of these issues with sexual and gender-based violence. We interviewed 11 key informants, facilitated 4 focus group discussions with refugee women, and conducted 21 in-depth interviews with Congolese women of reproductive age to understand better knowledge, attitudes, practices, and services. Using both inductive and deductive techniques, we employed a multi-phased analytic plan to identify content and themes and triangulate and interpret findings. Our results suggest that Congolese refugees in Uganda are unable to navigate the legal restrictions on abortion and are engaging in unsafe abortion practices. This appears to be the case for those living in both camps and urban areas. The legal restrictions on induced abortion pose a barrier to the provision of post-abortion care. Efforts to ensure access to comprehensive abortion care should be prioritised and providing information and support to women in need of post-abortion care is imperative.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".