Multifaceted Impact of the COVID-19 Pandemic and Lockdown on Physical and Mental Health: Insights from a Cross-Sectional Study
Bibliographic record
Abstract
Introduction The global COVID-19 pandemic and subsequent lockdowns have significantly impacted global wellbeing and highlighted the close link between mental and physical health. Social isolation and quarantine have proven to be major stressors, leading to emotional distress and unpredictable psychological consequences. Objectives We explored the pandemic’s impact on individuals’ physical and mental health and social relationships. Methods We conducted a cross-sectional study using a questionnaire which included among other socio-democratic questions, the Fear of COVID-19 Scale, the World Health Organization Quality-of-Life Scale (WHOQOL-BREF) and the Toronto Empathy Questionnaire (TEQ). Results A total of 511 adults (55.1% males) participated in this study. Participants reported increased social media use (more than 4-5 times/week) during the lockdown, which was associated with increased fear of COVID-19 and negative effects on mental and physical health, and social relationships (p<0.01). Conversely, non-work-related outings (once a week) were associated with lower fear (p<0.01) and better well-being (p<0.05). Higher fear, particularly for loved ones, was associated with negative effects. The level of physical health was moderate to high, with varying levels of satisfaction in different areas. Empathy correlated with increased fear (p<0.01) and reduced mobility (p<0.05). Conclusions The COVID-19 pandemic and lockdowns significantly affected physical and mental health, highlighting the importance of tailoring interventions for vulnerable populations and promoting adaptive coping strategies in times of crisis. Disclosure of Interest None Declared
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".