Understanding and attenuating pandemic-related disruptions: a plan to reduce inequalities in child development
Bibliographic record
Abstract
The Secretary General of the United Nations described the impact of COVID-19-related school closures as a "generational catastrophe." What will be the legacy of the 2020-2021 pandemic-related disruptions in 5, 10, 20 years from now, as regards education and well-being of children and youth? Addressing the disproportionate impact on those growing up in socio-economically disadvantaged areas or on those with pre-existing learning challenges is key to sustainable recovery. This commentary builds on the four literature reviews presented in this Special Section on a Pandemic Recovery Plan for Children and proposes strategies to understand and attenuate the impact of pandemic-related lockdown measures. Importantly, we need a monitoring strategy to assess indicators of child development in three areas of functioning: education and learning, health, and well-being (or mental health). Surveillance needs to begin in the critical prenatal period (with prenatal care to expectant parents), and extend to the end of formal high school/college education. Based on child development indicators, a stepped strategy for intervention, ranging from all-encompassing population-based health and education promotion initiatives to targeted prevention programs and targeted remedial/therapeutic interventions, can be offered. As proposed in the UN plan for recovery, ensuring healthy present and future generations involves a concerted and intensive intersectoral effort from the education, health, psychosocial services, and scientific communities.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".