The first 100 days: how has COVID-19 affected poor and vulnerable groups in India?
Bibliographic record
Abstract
In India, strict public health measures to suppress COVID-19 transmission and reduce burden have been rapidly adopted. Pandemic containment and confinement measures impact societies and economies; their costs and benefits must be assessed holistically. This study provides an evolving portrait of the health, economic and social consequences of the COVID-19 pandemic on vulnerable populations in India. Our analysis focuses on 100 days early in the pandemic from 13 March to 20 June 2020. We developed a conceptual framework based on the human right to health and the UN Sustainable Development Goals (SDGs). We analysed people's experiences recorded and shared via mobile phone on the voice platforms operated by the Gram Vaani COVID-19 response network, a service for rural and low-income populations now being deployed to support India's COVID-19 response. Quantitative and visual methods were used to summarize key features of the data and explore relationships between factors. In its first 100 days, the platform logged over 1.15 million phone calls, of which 793 350 (69%) were outbound calls related largely to health promotion in the context of COVID-19. Analysis of 6636 audio recordings by network users revealed struggles to secure the basic necessities of survival, including food (48%), cash (17%), transportation (10%) and employment or livelihoods (8%). Themes were mapped to shortfalls in 10 SDGs and their associated targets. Pre-existing development deficits and weak social safety nets are driving vulnerability during the COVID-19 crisis. For an effective pandemic response and recovery, these must be addressed through inclusive policy design and institutional reforms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".