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 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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".