C’est vraiment compliqué: a case study on the delivery of maternal and child health and nutrition interventions in the conflict-affected regions of Mali
Bibliographic record
Abstract
BACKGROUND: Mali is currently in the midst of ongoing conflicts which involve jihadist groups, rebels, and the state. This conflict has primarily centered in the North of the country. Humanitarian actors delivering services in these geographies must navigate the complex environment created by conflict. This study aimed to understand how humanitarian actors make decisions around health service delivery within this context. METHODS: The current case-study utilized a mixed methods approach and focused on Mopti, Mali's fifth administrative region and fourth largest in population. Latent content analysis was used to analyze interview transcripts guided by our research objectives and new concepts as they emerged. Indicators of coverage of health interventions in the area of maternal and child health and nutrition were compiled using Mali's National Evaluation Platform and are presented for the conflict and non-conflict regions. Development assistance estimates for Mali by year were obtained from the Developmental Assistance for Health Database compiled by the Institute for Health Metrics and Evaluation. Administrative data was compiled from the annual reports of Mali's Système Local d'Information Sanitaire (SLIS), Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). RESULTS: Our data suggests that the reaction of the funding mechanisms to the conflict in Mali was a major barrier to timely delivery of health services to populations in need and the nature of the conflict is likely a key modifier of such reaction patterns. Concerns have been raised about the disconnect between the very high administrative capacity of large NGOs that control the work, and the consequent burden it puts on local NGOs. Population displacement and inaccurate estimates of needs made it difficult for organizations to plan program services. Moreover, actors delivering services to populations in need had to navigate an unpredictable context and numerous security threats. CONCLUSIONS: Our study highlights the need for a more flexible funding and management mechanism that can better respond to concerns and issues arising at a local level. As the conflict in Mali continues to worsen, there is an urgent need to improve service delivery to conflict-affected populations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".