Associations of Suicide Stigma Internalization With Risk Factors of Suicidal Thoughts and Behaviors Among Chinese College Students: A Multi‐Wave Longitudinal Study
Bibliographic record
Abstract
INTRODUCTION: Suicide stigma is a multifaceted social issue with far-reaching consequences for mental health. While previous research has linked it to suicidal thoughts and behaviors (STBs), the roles of perceived and internalized forms of suicide stigma in influencing STBs remain unclear. METHOD: This study investigated the potential causal relationships between perceived and internalized suicide stigma, hopelessness, unbearable pain, and thwarted connectedness in relation to STBs among 546 Chinese college students (mean age = 20.92 years). Three-wave longitudinal data with a time gap of roughly 3 months were analyzed by using structural equation modeling. RESULTS: The results showed that the second-wave internalized stigma mediated the relationship between baseline perceived stigma and the third-wave unbearable pain, hopelessness, and thwarted connectedness, which are considered risk factors of STBs. Furthermore, the coexistence of unbearable pain and hopelessness, and the coexistence of unbearable pain and connectedness mediated the influences of perceived and internalized suicide stigma on STBs. CONCLUSION: These findings demonstrated that the temporal evolution of perceived and internalized suicide stigma contributes to risk factors predicting STBs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".