Understanding the factors affecting the humanitarian health and nutrition response for women and children in Somalia since 2000: a case study
Bibliographic record
Abstract
BACKGROUND: Somalia has been ravaged by more than two decades of armed conflict causing immense damage to the country's infrastructure and mass displacement and suffering among its people. An influx of humanitarian actors has sought to provide basic services, including health services for women and children, throughout the conflict. This study aimed to better understand the humanitarian health response for women and children in Somalia since 2000. METHODS: The study utilized a mixed-methods design. We collated intervention coverage data from publically available large-scale household surveys and we conducted 32 interviews with representatives from government, UN agencies, NGOs, and health facility staff. Qualitative data were analyzed using latent content analysis. RESULTS: The available quantitative data on intervention coverage in Somalia are extremely limited, making it difficult to discern patterns or trends over time or by region. Underlying sociocultural and other contextual factors most strongly affecting the humanitarian health response for women and children included clan dynamics and female disempowerment. The most salient operational influences included the assessment of population needs, donors' priorities, and insufficient and inflexible funding. Key barriers to service delivery included chronic commodity and human resource shortages, poor infrastructure, and limited access to highly vulnerable populations, all against the backdrop of ongoing insecurity. Various strategies to mitigate these barriers were discussed. In-country coordination of humanitarian health actors and their activities has improved over time, with federal and state-level ministries of health playing increasingly active roles. CONCLUSIONS: Emerging recommendations include further exploration of government partnerships with private-sector service providers to make services available throughout Somalia free of charge, with further research on innovative uses of technology to help reaches remote and inaccessible areas. To mitigate chronic skilled health worker shortages, more operational research is needed on the expanded use of community health workers. Persistent gaps in service provision across the continuum must be addressed, including for adolescents, for example. The is also a clear need for longer term development focus to enable the provision of health and nutrition services for women and children beyond those included in recurrent emergency response.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".