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Record W4401544703 · doi:10.1177/00302228241272648

Perceived and Internalized Stigma Towards Suicide and Their Roles in Suicidal Thoughts and Behaviors Among Chinese College Students

2024· article· en· W4401544703 on OpenAlexaff
Shunyan Lyu, Zixuan Guo, Sabrina Yanan Jiang, Yu Li

Bibliographic record

VenueOMEGA - Journal of Death and Dying · 2024
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsPsychologyStigma (botany)Psychological painSuicide preventionClinical psychologyPsychological interventionHuman factors and ergonomicsPoison controlPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Although perceived and internalized suicide stigma are considered risk factors for suicidal thoughts and behaviors (STBs), their specific roles in STBs are not well understood. This study examined the relationships among perceived and internalized suicide stigma, hopelessness, unbearable pain, suicidal desire, and suicide attempts in college students. A total of 1,387 Chinese college students (mean age: 22.22 years) completed the relevant scales. Structural equation modeling was used to determine the relationships of interest. The results showed that perceived stigma primarily had indirect impacts on suicidal desire through internalized stigma, which subsequently affected unbearable pain and hopelessness. The findings of this study suggest that the internalization of suicide stigma is an important predictor of STBs. These findings advocate for stigma interventions aimed at reducing internalized stigma as a potentially effective strategy for suicide prevention, as it may alleviate unbearable pain and hopelessness, which are significant contributors to suicidal desire and attempts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.341
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOMEGA - Journal of Death and DyingSame topicSuicide and Self-Harm StudiesFrench-language works237,207