A Perspective on Reprioritizing Children’s’ Wellbeing Amidst COVID-19: Implications for Policymakers and Caregivers
Bibliographic record
Abstract
The present work presents an analytical and investigatory view of the existing issues regarding COVID-19 with attention to children and their overall well-being during the second quarter of 2020. The authors conducted an extensive content analysis of media reports, government briefings, social platforms, and provide some recommendations to the policymakers and care providers for building more robust responses for the pandemic affected children. The article contributes to the existing field of study in the following ways. Firstly, the present manuscript describes the impact of COVID-19 on the psychosocial health of children. Secondly, the authors offered some outcome-based responses to policymakers and caregivers to mitigate the negative impact of the pandemic on COVID affected families and children. Thirdly, the article highlights the importance of social media, the role of storytelling, and using the concept of mandalas in handling the pandemic affected sensitive sections of the society. Lastly, the authors furnish some response initiatives to combat the novel COVID-19 pandemic based on real-world observations. These initiatives can influence policymakers as well as help caregivers to design efficient and adequate response programs for the pandemic affected 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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| 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".