Capacity and crisis: examining the state-level policy response to COVID-19 in Tamil Nadu, India
Bibliographic record
Abstract
The capacity of government agencies to develop effective policy responses to external shocks is an important area of focus for health policy processes, as illustrated by the coronavirus (COVID-19) pandemic. However, few empirical studies exploring the subnational capacity of governments and the influence of institutional, organizational and political factors in shaping the policy response to complex emergencies have been conducted. The purpose of this study is to examine the governance capacity to develop and implement a policy response to a major health emergency-COVID-19-in Tamil Nadu, India, and to understand the factors shaping governance capacity during the first and second waves (2020-21). Tamil Nadu offers a useful case for exploring governance capacity due to its long-standing public health institutions and previous experiences with disaster and outbreak response. We utilized three sources of data: (1) a review of key policy documents (n = 164); (2) a review of English-language media articles in the Indian press (n = 336); and (3) in-depth interviews with senior decision makers, technical experts and other stakeholders (n = 10). We present four key findings from this analysis. Firstly, Tamil Nadu's institutional framework enabled state-level governance capacity during an emergency of massive complexity, allowing for flexibility and nimbleness to adapt to evolving dynamics of centralization and decentralization over the course of the pandemic. Secondly, the ability to integrate public health expertise was circumscribed at important phases. Thirdly, while coordination with external experts was utilized extensively, engagement with civil society groups was perceived as limited. Fourthly, the electoral cycle was perceived by some to have constrained governance capacity at a critical point in the pandemic. By analysing the dynamics of state-level capacity in Tamil Nadu during a complex emergency, this study provides important learnings for other contexts globally regarding the drivers shaping capacity to develop and implement policy responses to crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".