Primary care for the urban poor in India during the pandemic: Uninterrupted management of non-communicable diseases and home-based care of patients with COVID-19 infection
Bibliographic record
Abstract
Problem The two waves of COVID-19 severely affected the healthcare system in India. The government responded to the first wave with a strict nationwide lockdown which disrupted primary care, including the management of non-communicable diseases (NCDs). The second wave overwhelmed healthcare facilities leading to inadequate access to hospital services. Collectively, these issues required urgent responses, including the adaptation of primary care. Approach The Low-Cost Effective Care Unit (LCECU) of Christian Medical College, Vellore (CMC) has a network of community volunteers, community health workers, an outreach nurse, social workers and doctors who operate clinics in six poorer areas of Vellore. The network adapted quickly, responding to the lockdown during the first wave and ensuring ongoing primary care for patients with non-communicable diseases. During the second wave, the team developed a system in collaboration with other CMC departments to provide home-based care for patients with COVID-19. Local setting The LCECU is a 48-bed unit of the Department of Family Medicine, part of the 3,000-bed CMC. It originated in 1982, aiming to care for the poor populations of Vellore town. It has been actively working among urban communities since 2002, with a focus on delivering Community Oriented Primary Care (COPC), for six poor urban communities since 2016. Relevant changes During the first wave of COVID the LCECU team ensured patients with NCDs had uninterrupted primary care and medications by visiting them in their homes. The team also addressed food insecurity by organizing a daily lunch service for 600 people for over 2 months. In the second wave, the team responded to community needs by organizing and delivering home-based care to monitor patients affected by COVID-19. Lessons learned The COVID-19 pandemic raises many questions about the preparedness of health systems for disasters that disproportionately affect marginalized populations globally. COVID-19 is only one of the many potential disasters, including non-communicable diseases, mental health problems, pollution, climate change, and lifestyle illness. There is an urgent need to study models of care that support vulnerable communities in an accessible, cost-effective, and patient-oriented way, particularly in low- and middle-income countries. This paper outlines lessons on how the LCECU team addressed disaster management: 1. The COVID-19 pandemic has highlighted the importance of primary care-based rapid response interventions in disaster management. 2. The LCECU model demonstrated the effectiveness of a primary care intervention based on pre-existing networks and familiarity between primary care teams and the community. 3. Establishing community-based health care via interdisciplinary teams, including community health workers, community volunteers, outreach nurses, and doctors, is key. 4. Addressing other social determinants of health, such as food insecurity, is an important component of care delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".