APPRAISAL OF RESILIENCE AND ADJUSTMENT STRATEGIES IN NIGERIA'S HEALTH SUPPLY CHAIN IN NORTHEAST NIGERIA AMID HUMANITARIAN CRISES
Bibliographic record
Abstract
Protracted conflict in Northeast Nigeria, particularly in Borno and Adamawa States, has severely threatened the health supply chain, undermining access to essential medicines and services, thus the need for resilience in the face of these challenges. This study appraised the resilience and adjustment strategies in Nigeria's health supply chain in northeast Nigeria amid humanitarian crises. The population included health supply chain stakeholders across Borno and Adamawa States with a sample size of 200 selected using the purposive sampling technique. Quantitative data were collected using questionnaire while Qualitative insights were obtained through key informant interviews with officials from government agencies, NGOs, and development partners. Quantitative data were analyzed using percentage, mean and regression statistics with the aid of the Statistical Product for Service Solution (SPSS V-27), while qualitative responses were thematically coded. Analysis was framed using a resilience systems theory model, emphasizing robustness, redundancy, resourcefulness, and rapidity. The result showed that less than a quarter 28(14.0%) indicated that the health supply chain was resilient. The primary challenges of the organization identified were: insecurity/conflict (97.0%), supply shortage (96.0%), limited funding (94.5%), infrastructure breakdown (78.5%), lack of trained personnel (66.5%), and poor communication system (58.5%). Resilience strategies employed by organization were: use of digital monitoring system (90.5%), buffer stock maintenance (88.5%), alternative supplier networks (84%), local sourcing during emergencies (81%), deployment of mobile storage unit (69%) and collaboration with humanitarian partners (61.5%). More than one quarter (34.0%) indicated that the current resilience strategy was effective. The Main limitations or gaps in organization’ current resilience strategies identified were limited emergency funding (96.5%), inadequate storage facilities (94.0%), weak distribution networks (88.0%), inconsistent supply chain (79.5%), lack of policy support (75.5%) and delayed decision-making processes (72.0%). Close to three quarter (73.5%) indicated that collaborations and partnerships is extremely criticalto organization’s resilience. The study concludes that the health supply chain in Northeast Nigeria operates within an exceedingly complex, volatile, and perpetually challenging environment, fundamentally characterized by prolonged and severe humanitarian crises. It recommends strengthened coordination among government and partners is critical for sustainable impact
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".