Fear of COVID-19 and its association with mental health-related factors: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The severity of COVID-19 remains high worldwide. Therefore, millions of individuals are likely to suffer from fear of COVID-19 and related mental health factors. AIMS: The present systematic review and meta-analysis aimed to synthesize empirical evidence to understand fear of COVID-19 and its associations with mental health-related problems during this pandemic period. METHOD: Relevant studies were searched for on five databases (Scopus, ProQuest, EMBASE, PubMed Central, and ISI Web of Knowledge), using relevant terms (COVID-19-related fear, anxiety, depression, mental health-related factors, mental well-being and sleep problems). All studies were included for analyses irrespective of their methodological quality, and the impact of quality on pooled effect size was examined by subgroup analysis. RESULTS: The meta-analysis pooled data from 91 studies comprising 88 320 participants (mean age 38.88 years; 60.66% females) from 36 countries. The pooled estimated mean of fear of COVID-19 was 13.11 (out of 35), using the Fear of COVID-19 Scale. The associations between fear of COVID-19 and mental health-related factors were mostly moderate (Fisher's z = 0.56 for mental health-related factors; 0.54 for anxiety; 0.42 for stress; 0.40 for depression; 0.29 for sleep problems and -0.24 for mental well-being). Methodological quality did not affect these associations. CONCLUSIONS: Fear of COVID-19 has associations with various mental health-related factors. Therefore, programmes for reducing fear of COVID-19 and improving mental health are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".