Local governance in the COVID-19 response: Challenges, strategies, and lessons – a multinational integrative review
Bibliographic record
Abstract
This integrative review aims to examine the role of local governance in addressing the COVID-19 pandemic across diverse countries. Based on 39 scientific articles published between 2020 and April 2025 in the following databases: PubMed, Web of Science, Scopus, Science Direct, and Google Scholar. Findings reveal that the effectiveness of public health crisis responses was intrinsically linked to local governments’ adaptive capacity, intergovernmental coordination, and social participation. Six key thematic categories were identified: (i) adaptive capacity and resilience; (ii) coordination structures; (iii) enabling factors (resources, leadership); (iv) structural challenges (fragmentation, underfunding); (v) social participation; and (vi) contextual variations. Countries such as China, South Korea, and Bangladesh demonstrated effective local-community articulation, whereas Brazil, Sweden, and Zimbabwe faced limitations due to centralization and federative weaknesses. The study concludes that decision-making autonomy, adequate funding, and multi-level cooperation are critical for effective responses to health crises. It recommends strengthening local institutional arrangements for future emergencies. As one of the pioneering multinational comparative analyses of local governance during COVID-19, this review provides an analytical framework applicable to future health crises, offering practical insights for designing resilient decentralized governance 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".