Health and economic implications of the ongoing coronavirus disease (COVID-19) pandemic on women and children in Africa
Bibliographic record
Abstract
The coronavirus disease (COVID-19) pandemic continues to pose major health and economic challenges for many countries worldwide. Particularly for countries in the African region, the existing precarious health status resulting from weak health systems have made the impact of the pandemic direr. Although the number of the COVID-19 infections in Africa cannot be compared to that of Europe and other parts of the world, the economic and health ramifications cannot be overstated. Significant impacts of the lockdowns during the onset of the pandemic caused disruptions in the food supply chain, and significant declines in income which decreased the affordability and consumption of healthy diets among the poor and most vulnerable. Access and utilization of essential healthcare services by women and children were also limited because of diversion of resources at the onset of the pandemic, limited healthcare capacity, fear of infection and financial constraint. The rate of domestic violence against children and women also increased, which further deepened the inequalities among these groups. While all African countries are out of lockdown, the pandemic and its consequent impacts on the health and socio-economic well-being of women and children persist. This commentary discusses the health and economic impact of the ongoing pandemic on women and children in Africa, to understand the intersectional gendered implications within socio-economic and health systems and to highlight the need for a more gender-based approach in response to the consequences of the pandemic in the Africa region.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".