From Insecurity to Health Service Delivery: Pathways and System Response Strategies in the Eastern Democratic Republic of the Congo
Bibliographic record
Abstract
The provinces of North and South Kivu in eastern Democratic Republic of the Congo (DRC) have experienced insecurity since the 1990s. Without any solution to the conflict in sight, health actors have adapted their interventions to maintain some level of health service provision. We reflect on the health system resilience in the Kivu provinces in response to chronic levels of insecurity. Using qualitative interviews of health care providers from local government, United Nations agencies, and international nongovernmental organizations, we identify the mediating factors through which insecurity affects both service quality and delivery and investigate the strategies adopted to sustain service provision.Three main drivers linking insecurity and health service quality and delivery emerged: via violence, mobility restrictions, and resources availability. The effect of these drivers is mediated by several system or individual-level factors. Two factors were reported in each pathway: health care workforce availability and drug/equipment accessibility. Human resources were affected differently by each driver: in terms of willingness to be stationed in a certain area (violence), capacity to access the health facility (mobility), and sustainability and motivation of conducting duties (resources). Similarly, the presence of drugs/equipment varied in case of looting or damages (violence), delays in delivery (mobility), or delays in procurement (resources). While these mediators are not surprising, their identification allows the design of appropriate response strategies. The majority of the reported solutions attempt to address the lack of human resources and reflect absorptive capacity. Adaptive capacity characterizes the attempts to address lack of access (contingency plan, mobile clinics, maternity waiting homes, and security drugs). Finally, interventions to address insecurity can be classified as transformative. Health actors in eastern DRC have shown some capacity to adapt, adjust, and transform due to insecurity. Further research is needed to measure the effectiveness of such strategies to provide guidance to increasingly vulnerable health systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".