Comparison of mental health symptoms before and during the covid-19 pandemic: evidence from a systematic review and meta-analysis of 134 cohorts
Bibliographic record
Abstract
OBJECTIVE: To synthesise results of mental health outcomes in cohorts before and during the covid-19 pandemic. DESIGN: Systematic review. DATA SOURCES: Medline, PsycINFO, CINAHL, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, medRxiv, and Open Science Framework Preprints. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies comparing general mental health, anxiety symptoms, or depression symptoms assessed from 1 January 2020 or later with outcomes collected from 1 January 2018 to 31 December 2019 in any population, and comprising ≥90% of the same participants before and during the covid-19 pandemic or using statistical methods to account for missing data. Restricted maximum likelihood random effects meta-analyses (worse covid-19 outcomes representing positive change) were performed. Risk of bias was assessed using an adapted Joanna Briggs Institute Checklist for Prevalence Studies. RESULTS: 0.11, 95% confidence interval -0.00 to 0.22) or anxiety symptoms (0.05, -0.04 to 0.13), but depression symptoms worsened minimally (0.12, 0.01 to 0.24). Among women or female participants, general mental health (0.22, 0.08 to 0.35), anxiety symptoms (0.20, 0.12 to 0.29), and depression symptoms (0.22, 0.05 to 0.40) worsened by minimal to small amounts. In 27 other analyses across outcome domains among subgroups other than women or female participants, five analyses suggested that symptoms worsened by minimal or small amounts, and two suggested minimal or small improvements. No other subgroup experienced changes across all outcome domains. In three studies with data from March to April 2020 and late 2020, symptoms were unchanged from pre-covid-19 levels at both assessments or increased initially then returned to pre-covid-19 levels. Substantial heterogeneity and risk of bias were present across analyses. CONCLUSIONS: High risk of bias in many studies and substantial heterogeneity suggest caution in interpreting results. Nonetheless, most symptom change estimates for general mental health, anxiety symptoms, and depression symptoms were close to zero and not statistically significant, and significant changes were of minimal to small magnitudes. Small negative changes occurred for women or female participants in all domains. The authors will update the results of this systematic review as more evidence accrues, with study results posted online (https://www.depressd.ca/covid-19-mental-health). REVIEW REGISTRATION: PROSPERO CRD42020179703.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".