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Record W4417153585 · doi:10.1177/00332941251400723

The Relationship Between Attitudes Toward Suicide and Suicidal Ideation and Attempts Among Chinese College Students: The Mediating Role of Depression

2025· article· en· W4417153585 on OpenAlexaff
Shunyan Lyu, Zixuan Guo, Sabrina Yanan Jiang, Yu Li

Bibliographic record

VenuePsychological Reports · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsSuicidal ideationSuicide preventionDepression (economics)Suicide ideationHuman factors and ergonomicsPoison controlInjury prevention

Abstract

fetched live from OpenAlex

Attitudes toward suicide are complex and multifaceted. Their predictive roles and how they interact with mental health in suicidal ideation and suicide attempts are not well understood. Elaborating on attitudes toward suicide can help contribute to suicide prevention. The current study examined whether and how attitudes toward suicide and depression levels are involved in suicidal ideation and suicide attempts among college students. A cross-sectional survey was administered to 1,427 Chinese college students (61.32% females; mean age, 22.42 years), of whom 570 reported suicidal ideation. Participants completed questionnaires to assess their endorsed attitude toward suicide, stigmatized attitude toward suicide, self-stigma toward suicide, depression, suicidal ideation, and suicide attempts. Structural equation modeling revealed that the associations between the three types of attitudes, i.e., endorsed attitude, stigmatized attitude, and self-stigma, and suicidal ideation and suicide attempts were fully or partially mediated by depression. The results suggested that attitudes toward suicide significantly influence suicidal behaviors through depression levels and future suicide prevention efforts should consider these influences.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.016
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.048
GPT teacher head0.409
Teacher spread0.360 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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