Impact of “Long Covid” on Children: Global and Hong Kong Perspectives
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease (COVID-19) pandemic spares no nation or city, and the virus is responsible for the escalating incidence and mortality worldwide. OBJECTIVE: This article reviews the impact of "Long Covid" on Children. METHODS: A PubMed search was conducted in December 2021 in Clinical Queries using the key terms "COVID-19" OR "long COVID". The search was restricted to children and adolescent aged < 18 years and English literature. RESULTS: Many large-scale studies have provided strong scientific evidence as to the detrimental and irreversible sequelae of COVID-19 on the health, psychology, and development of affected children. Many insights into managing this disease can be obtained from comparing the management of influenza. COVID-19 is generally a mild respiratory disease in children. Several syndromes, such as multisystem inflammatory syndrome in children (MIS-C) and COVID toe, are probably not specific to SARS-CoV-2. "Long COVID", or the long-term effects of SARS-CoV-2 infection, or the prolonged isolation and containment strategies on education and psychosocial influences on children associated with the pandemic, are significant. CONCLUSION: Healthcare providers must be aware of the potential effects of quarantine on children's mental health. More importantly, healthcare providers must appreciate the importance of the decisions and actions made by governments, non-governmental organizations, the community, schools, and parents in reducing the possible effects of this situation. Multifaceted age-specific and developmentally appropriate strategies must be adopted by healthcare authorities to lessen the negative impact of quarantine on the psychological well-being of 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".