Inequalities in the reported impacts of COVID-19 on child health: A narrative review
Bibliographic record
Abstract
Background: The world battled with children’s needs during the pre-COVID-19 era, and their health and educational needs were amplified during the COVID-19 pandemic. This article highlights the reported impact of the pandemic on child health, the inequalities observed, the lessons learnt, and the way forward in the post-COVID era. Method: A narrative literature review was conducted. Articles from Google Scholar and PubMed were searched from 2015 upward. The reference lists of the included articles were also searched for more relevant studies. A descriptive analysis of the included studies was conducted to highlight the reported impact of the COVID-19 pandemic on child health. Results: During the pandemic, every child was not affected equally. Inequalities in child physical, mental and social health were observed more in low and middle-income countries (LMICs). Similarly, child nutrition was adversely affected as school feeding programs were disrupted. Although the education of children was adversely affected globally, the impact was more in LMICs, where digital learning was not well-developed. This was also worse in insurgent countries with many out-of-school children. Conclusion: Efforts should be geared towards meeting children's physical, mental and social needs during pandemics, with a strong focus on children from developing countries. Similarly, the education of children should not be neglected. As efforts are directed towards meeting the needs of adults post-COVID, inequalities observed in child health during the pandemic should be addressed such that no child is left behind.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".