Impact on Public Health Nutrition Services Due to COVID-19 Pandemic in India: A Scoping Review of Primary Studies on Health and Social Security Determinants Affecting the First 1000 Days of Life
Bibliographic record
Abstract
CONTEXT: COVID-19 was declared 'a global pandemic' by the World Health Organization in March 2020. India's lockdown, one of the harshest in the world, came with additional challenges for women. This paper aims to assess the impact of COVID-19 pandemic-related pathways on the first thousand days of life in the Integrated Child Development Scheme and the public distribution ecosystem in India. DATA SOURCES: Using Cochrane guidelines, electronic databases, namely Google Scholar and PubMed-NCBI, were searched for evidence between 1 March 2020 and 1 May 2022. A total of 73 studies were identified in initial search; 20 met the inclusion criteria and, thus, were included in the research analysis. Primary studies were conducted throughout pan-India in rural, urban, and semi-urban areas to study the impact of COVID-19 pandemic-related pathways on the first 1000 days of life. The impact of social security, food insecurity, service delivery, nutrition of pregnant and nursing mothers (P&NMs), and infant and young child feeding (IYCF) varied between geographies and within geographies. Most of the primary studies were conducted at small scale, while only three studies were pan-Indian. The majority of studies were conducted on the mental health of P&NMs and pre-natal and post-natal service delivery disruption. The paucity of the available literature highlights the need to undertake research on the impact of the COVID-19 pandemic-related pathways on 1000 days of life in India and worldwide. The best implementation practices were observed where cross-sectional programs were carried out in relation to health services and social security for P&NMs and children.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".