COVID-19 and Its Impact on Mental Health Across All Age Groups
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted mental health across all age groups, with varying degrees of severity depending on age and life circumstances. This article explores these differences, revealing that while older adults experienced the highest physical health risks, they often demonstrated greater psychological resilience, reporting lower levels of anxiety and depression compared to younger populations. In contrast, children and adolescents faced considerable psychological challenges, including heightened anxiety, sleep disturbances, and emotional distress due to school closures, social isolation, and disrupted routines. Adolescents, particularly those in unsupportive home environments, experienced increased psychological distress and reduced access to affirming communities and mental health services. Among adults, widespread psychological distress stemmed from job losses, economic insecurity, caregiving burdens, and fear of illness. Younger and middle-aged adults reported higher anxiety levels than older adults, partly due to financial strain and balancing work-from-home duties with childcare. Frontline healthcare workers experienced extreme mental health impacts, including burnout and post-traumatic stress symptoms. Understanding these age-specific mental health impacts is crucial for developing targeted interventions to support psychological well-being during pandemics and other public health crises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".