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Record W4310527895 · doi:10.3389/fpsyt.2022.1012298

Associations between anxiety, depression, and risk of suicidal behaviors in Chinese medical college students

2022· article· en· W4310527895 on OpenAlexaff
Jia Li, Yaru Zhang, Bella Siu Man Chan, Sun Nee Tan, Jianping Lu, Xuerong Luo, Yanmei Shen, Xiangyang Zhang

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnxietyDepression (economics)Clinical psychologyDepressive symptomsPsychiatryPsychologyMedicine

Abstract

fetched live from OpenAlex

Background: Previous studies have established a strong association between depression and suicidal behaviors, yet the relationship between anxiety and suicidal behaviors remains unclear. This study examines whether anxiety and depression are independent risk factors for suicidal behaviors in medical college students, and further, whether anxiety may increase the greater risk of suicidal behaviors (SB) in participants with depression. Methods: This cross-sectional study was conducted among 4,882 medical students. Demographic information, anxiety, and depression data were collected using online questionnaires or through a widely used social media app named WeChat. Results: Anxiety and depression were independent risk factors for suicidal behaviors, and levels of risk correlated positively with the severity of both anxiety and depressive symptoms. A dose-response relationship was identified between the severity of anxiety and the risk of SB, as well as the severity of depression and SB. Furthermore, anxiety increased the risk of suicidal behaviors in participants with depression, with a dose-response relationship between the severity of anxiety symptoms and the risk of SB. Conclusion: The findings highlight the importance of screening for anxiety and depressive symptoms in medical college students, as well as reducing anxiety in addition to depressive symptoms in treatment. This study provides valuable data as a reference for clinicians for suicide risk assessments.

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)0.001
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 score0.055
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.316
Teacher spread0.307 · 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

Citations27
Published2022
Admission routes1
Has abstractyes

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