Understanding Health System Resilience in responding to a pandemic: experience and lessons from an evolving context of federalization in Nepal
Bibliographic record
Abstract
Abstract Introduction The COVID-19 pandemic has tested the resilience capacities of health systems worldwide and has highlighted the need to understand the concept, pathways, and elements to resilience in different country contexts. In this study, we assessed the health system response to COVID-19 and examined the processes of policy formulation, communication and implementation at the three tiers of government in Nepal, including the dynamic interactions between tiers. Nepal was experiencing the early stages of federalization reform when COVID-19 pandemic hit the country and clarity in roles and capacity to implement functions were the prevailing challenges especially among the subnational governments. Methods We adopted a cross-sectional exploratory design, using mixed methods. We carried out a document review of all policy documents introduced in response to COVID-19 from January-December 2020, and collected qualitative data through 22 key informant interviews at three tiers of government, during January-March 2021. Two municipalities were purposively selected for data collection in Lumbini province. Our analysis is based on a resilience framework that has been developed by our research project, ReBUILD for Resilience, which helps to understand pathways to health system resilience through absorption, adaptation and transformation. Results In the newly established federal structure, the existing emergency response structure and plans were utilized, which were yet to be tested in the decentralized structure. Federal government effectively led the policy formulation process, with minimal engagement of sub-national governments. The local governments did not demonstrate resilience capacities, due to the novelty of the federal system and their consequent lack of experience, confusion on roles, insufficient management capacity and governance structures at local level and limited availability of human, technical and financial resources. Conclusions The study findings emphasize the importance of strong and flexible governance structures and strengthened capacity of subnational governments to effectively manage pandemics. The study elaborates on the key areas and pathways that contribute to resilience capacities of health systems from the experience of Nepal. We draw out lessons for other fragile and shock-prone settings.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".