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Record W4323652410 · doi:10.1136/bmj-2022-074224

Comparison of mental health symptoms before and during the covid-19 pandemic: evidence from a systematic review and meta-analysis of 134 cohorts

2023· review· en· W4323652410 on OpenAlexafffund
Ying Sun, Yin Wu, Suiqiong Fan, Tiffany Dal Santo, Letong Li, Xiaowen Jiang, Kexin Li, Yutong Wang, Amina Tasleem, Ankur Krishnan, Chen He, O Bonardi, Jill Boruff, Danielle B. Rice, Sarah Markham, Brooke Levis, Marleine Azar, Ian Thombs-Vite, Dipika Neupane, Branka Agic, Christine Fahim, Michael S. Martin, Sanjeev Sockalingam, Gustavo Turecki, Andrea Benedetti, Brett D. Thombs

Bibliographic record

VenueBMJ · 2023
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMinistry of Community Safety and Correctional ServicesOttawa Public HealthUniversity of TorontoCentre for Addiction and Mental HealthPublic Health OntarioSt. Joseph’s Healthcare HamiltonDouglas Mental Health University InstituteMcGill University Health CentreUniversity of OttawaMcGill UniversityMcMaster UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsMeta-analysisPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Mental healthMedicineSystematic reviewMEDLINECoronavirus InfectionsData sciencePsychiatryVirologyComputer sciencePathologyBiologyDiseaseOutbreak

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0110.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.429
GPT teacher head0.578
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations281
Published2023
Admission routes2
Has abstractyes

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