Health service resilience in Yobe state, Nigeria in the context of the Boko Haram insurgency: a systems dynamics analysis using group model building
Bibliographic record
Abstract
BACKGROUND: Yobe State has faced severe disruption of its health service as a result of the Boko Haram insurgency. A systems dynamics analysis was conducted to identify key pathways of threat to provision and emerging pathways of response and adaptation. METHODS: Structured interviews were conducted with 39 stakeholders from three local government areas selected to represent the diversity of conflict experience across the state: Damaturu, Fune and Nguru, and with four officers of the PRRINN-MNCH program providing technical assistance for primary care development in the state. A group model building session was convened with 11 senior stakeholders, which used participatory scripts to review thematic analysis of interviews and develop a preliminary systems model linking identified variables. RESULTS: Population migration and transport restrictions have substantially impacted access to health provision. The human resource for health capability of the state has been severely diminished through the outward migration of (especially non-indigenous) health workers and the suspension of programmes providing external technical assistance. The political will of the Yobe State government to strengthen health provision - through lifting a moratorium on recruitment and providing incentives for retention and support of staff - has supported a recovery of health systems functioning. Policies of free-drug provision and decentralized drug supply appear to have been protective of the operation of the health system. Community resources and cohesion have been significant assets in combatting the impacts of the insurgency on service utilization and quality. Staff commitment and motivation - particularly amongst staff indigenous to the state - has protected health care quality and enabled flexibility of human resource deployment. CONCLUSIONS: A systems analysis using participatory group model building provided a mechanism to identify key pathways of threat and adaptation with regard to health service functioning. Generalizable systems characteristics supportive of resilience are suggested, and linked to wider discussion of the role of factors such as diversity, self-regulation and integration.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".