Potential psychosocial impact of COVID-19 on children: a scoping review of pandemics and epidemics
Bibliographic record
Abstract
Background Physical distancing and health measures, such as school closures and work-at-home mandates, implemented to mitigate the transmission of COVID-19, will likely have far-reaching impacts on children’s psychosocial health and well-being. We aimed to examine extant literature on pandemics and epidemics to draw comparisons regarding COVID-19 on children’s psychosocial health and secondary outcomes, including nutritional, financial and child safety outcomes. Methods Articles were searched within the Medline, Global Health, PsycINFO, and CINAHL databases on 11 June 2020. Grey literature was also examined from the World Health Organization (WHO) and United Nations Children’s Fund (UNICEF) until 24 July 2020. Results A total of 8,332 articles were screened for eligibility by two independent reviewers. Of these, 7,919 and 413 articles were from academic databases and additional sources, respectively. Results on child outcomes were extracted and collated. Seventy-three articles met inclusion criteria. Children have faced significant challenges with 12% of articles indicating loneliness/depression, 19% anxiety, 7% grief, 10% stress-related disorders, 25% child abuse, 8% family conflict, and 12% stigma during pandemics/epidemics. Furthermore, 25% of articles indicated economic challenges, 23% negative academic impacts, 33% improper nutrition, and 21% reduced opportunities for play/increased screen time. These challenges were exacerbated among children who were female, have a disability, or who were a migrant/displaced child. Conclusions Pandemics and epidemics have had diverse and widespread negative consequences for children. Findings can inform the development and implementation of resources during the COVID-19 pandemic to protect child health and well-being.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".