Children and adolescents’ mental health in pandemics and confinement: A scoping review of vulnerability factors and repercussions
Bibliographic record
Abstract
Children and adolescents are a population at particular risk of experiencing adverse mental health repercussions related to pandemics. To understand vulnerability factors and repercussions of pandemics and related sanitary measures on children and adolescents' mental health, we performed a scoping review to examine and synthesize literature. In total, 66 articles were included. Results present: (1) factors that increase vulnerability to adverse mental health repercussions (e.g., having a pre-existing mental health condition, social isolation, low socio-economic status, parental distress, and overexposure to media content) and (2) specific mental health repercussions (e.g., anxiety, fear, depression, and externalizing behaviors). Addressing concerns underlined in this review could contribute to preventing further negative mental health repercussions of pandemics for children and adolescents and better prepare governments and professionals to address these highly challenging situations. Recommendations for practice include enhancing healthcare professionals' awareness about possible detrimental repercussions pandemics and sanitary measures have on children and adolescents' mental health, assessing changes for those with pre-existing mental health conditions, allocating funding for telehealth research, and providing greater support to healthcare providers.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".