Covid-19 lockdown governance in Uttar Pradesh, India: a call for equity?
Bibliographic record
Abstract
The COVID-19 pandemic has reflected the weaknesses in the already collapsing health systems of the countries. In India, there has been a mass exodus of migrants during the lockdown, witnessed by the world. We undertook a qualitative data analysis to examine the governance arrangements, decision-making processes and implementation of policies during the pandemic in the state of Uttar Pradesh, India. Methods We did a qualitative study using thematic analysis. The participants (n = 16) were recruited from the district(n = 4), state (n = 6) and centre (n = 6) level using purposive sampling. They participated in in-depth interviews between May 2020-July 2020 by phone/zoom. Interviews were transcribed verbatim, and data were analysed using Dedoose software. Ethical approval was obtained from the King George Medical College, Lucknow, Uttar Pradesh, vide registration ECR/262/Inst/UP/2013/RR-19 Findings We recruited participants (15 males and 1 female), and five theme categories emerged from the data analysis. These were: 1) Centralized decision-making with decentralized implementation, 2) Consultative processes for decision-making but little emphasis on consensus building, 3) Informal channels of communication and enhanced intersectoral coordination, 4) Community involvement leading to transparency, and 5) Enhanced inequities during the crisis. Results Lessons learnt from examining governance and decision-making in one state of India reveal the need for reducing inequities and attention to primary ethical considerations in times of humanitarian crisis. Going forward, we need to work towards building resilience into the health system and increasing the role of decentralized participatory decision-making and governance. The use of digital technology and social media platforms greatly facilitated the response during the pandemic and can be capitalized on more in the future as a global health policy matter.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".