COVID-19 pandemic-related disruptions to maternal, child and adolescent health and nutrition services in Latin America and the Caribbean
Bibliographic record
Abstract
Abstract Background The ramifications of the COVID-19 pandemic have extended far beyond the direct impacts (cases and deaths); acute pandemic control measures and fear of infection have affected populations more broadly, and vulnerable populations—including women, children and adolescents—have been the most affected. This report seeks to examine the effect that the COVID-19 pandemic has had on health systems in Latin America and the Caribbean, with a focus on ten countries, both overall and with a specific view on maternal, child and adolescent health and nutrition services. The report also presents evidence-based policy recommendations to mitigate these effects. Methods We created an analysis framework based on a literature review and publicly available data, and validated with expert interviews. We used this analysis framework to compare the extent of disruptions to maternal, child and adolescent health and nutrition services during the COVID-19 pandemic. Results Overall, we found evidence of significant service disruption across three core domains: antenatal services, intra- and post-partum services and child and adolescent services with possible associations on maternal, infant and child health outcomes. Four key policy implications are presented for consideration to manage risks of service disruption and increase the resilience of health systems more broadly in Latin American and the Caribbean. Conclusions Owing to the developing nature of many of the health systems in the countries studied, the findings were affected by missing data and therefore should be interpreted with caution and supplemented with further research.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".