Training reproductive health professionals in a post-conflict environment: exploring medical, nursing, and midwifery education in Mogadishu, Somalia
Bibliographic record
Abstract
Following two decades of civil war, Somalia recently entered the post-conflict rebuilding phase that has resulted in the rapid proliferation of higher education institutions. Given the high maternal mortality ratio, the federal government has identified the reproductive health education of health service professionals as a priority. Yet little is known about the coverage of contraception, abortion, pregnancy, childbirth, and sexual and gender-based violence (SGBV) in medicine, nursing, or midwifery. In 2016, we conducted a multi-methods study to understand the reproductive health education and training landscape and identify avenues by which development of the next generation of health service professionals could be improved. Our study comprised two components: interviews with 20 key informants and 7 focus group discussions (FGDs) with 48 physicians, nurses, midwives, and medical students. Using the transcripts, memos, and field notes, we employed a multi-phased approach to analyse our data for content and themes. Our findings show that reproductive health education for medical and nursing students is inconsistent and significant content gaps, particularly in abortion and SGBV, exist. Students have few clinical training opportunities and the overarching challenges plaguing higher education in Somalia also impact health professions programmes in Mogadishu. There is currently a window of opportunity to develop creative strategies to improve the breadth and depth of evidence-based education and training, and multi-stakeholder engagement and the promotion of South-South exchanges appear warranted.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".