A Systematic Review and Meta-Analysis on Mental Illness Symptoms in Spain in the COVID-19 Crisis
Bibliographic record
Abstract
Abstract Objective This paper systematically reviews and assesses the prevalence of anxiety, depression, and insomnia symptoms in the general population, frontline healthcare workers (HCWs), and adult students in Spain during the COVID-19 crisis. Data sources Articles in PubMed, Embase, Web of Science, PsycINFO, and medRxiv from March 2020 to February 6, 2021. Results The pooled prevalence of anxiety symptoms in 23 studies comprising a total sample of 85,560 was 20% (95% CI: 15% - 25%, I2 = 99.9%), that of depression symptoms in 23 articles with a total sample comprising of 86,469 individuals was 23% (95% CI: 18% - 28%, I2 = 99.8%), and that of insomnia symptoms in 4 articles with a total sample of 915 were 52% (95% CI: 42-64%, I2 = 88.9%). The overall prevalence of mental illness symptoms in frontline HCWs, general population, and students in Spain are 42%, 19%, and 50%, respectively. Discussion The accumulative evidence from the meta-analysis reveals that adults in Spain suffered higher prevalence rates of mental illness symptoms during the COVID-19 crisis with a significantly higher rate relative to other countries such as China. Our synthesis reveals high heterogeneity, varying prevalence rates and a relative lack of studies in frontline and general HCWs in Spain, calling future research and interventions to pay attention to those gaps to help inform evidence-based mental health policymaking and practice in Spain during the continuing COVID-19 crisis. The high prevalence rates call for preventative and prioritization measures of the mental illness symptoms during the Covid-19 pandemic.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".