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Record W4408317482 · doi:10.1080/07481187.2025.2476981

Comparison of suicidal behavior among Chinese university students before and during the COVID-19 pandemic: Findings from a two-wave cross-sectional study

2025· article· en· W4408317482 on OpenAlexafffund
Zhi-Ying Yao, Xiaomei Xu, Changgui Kou, Xinting Wang, Bao-Peng Liu, Shengli Cheng, Gao Jian-guo, Bob Lew, Josephine Pui‐Hing Wong, Kenneth Fung, Cun-Xian Jia

Bibliographic record

VenueDeath Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsCoronavirus disease 2019 (COVID-19)PandemicCross-sectional study2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyClinical psychologyMedicineVirologyOutbreak

Abstract

fetched live from OpenAlex

This study explores the changes in suicidal behavior among Chinese university students before and during the COVID-19 pandemic. The prevalence of lifetime suicide plan, lifetime suicidal ideation, and 12-month suicidal ideation among Chinese university students was higher during the COVID-19 pandemic compared to before the pandemic. The prevalence of lifetime suicidal ideation did not increase among students with high family economic status, whereas the prevalence of lifetime suicide attempt increased among students with poor academic performance. Women, urban household registration, poor mental health status, poor academic performance, anxiety symptoms, and depressive symptoms were associated with an increased risk of suicidal ideation. Intervention measures targeted at reducing the academic pressure and financial difficulties of university students ought to constitute a crucial component of universities' efforts to prevent student suicidal behavior following public health crises. A more representative, long-term, longitudinal study should be used to track suicidal behavior among university students.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.101
GPT teacher head0.447
Teacher spread0.346 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes2
Has abstractyes

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