Relationships between negative life events and suicidal ideation among youth in China: The direct and moderating effects of offline and online social support from gender perspective
Bibliographic record
Abstract
Background: Suicidal ideation was proved to be a critical precondition leading to the occurrence of subsequent suicidal behavior. Studies have confirmed that negative life events and forms of social support that youth are experiencing in the current socio-cultural context might have unique impacts on their suicidal ideation. However, the specific mechanism is relatively underexplored. Objective: We sought to investigate the impacts of offline and online social supports on Chinese students' suicidal ideation under the pressure of various negative life events, as well as potential gender differences in these relationships. Methods: Participants were 2,018 middle - high school and university students from Northwestern China, who completed a demographics questionnaire and self-report measures of negative life events, social support, and suicidal ideation. Results: Offline social support had a significant direct effect on suicidal ideation across genders. Among male youth, offline social support only had a moderating effect on the relationship between punitive negative life events and suicidal ideation. Among female youth, offline social support had a significant moderating effect on suicidal ideation under the pressure of all types of negative life events; Online social support only had a significant direct effect on female youth's suicidal ideation, although it did significantly moderate the relationship between all types of negative life events and suicidal ideation, across genders. Conclusion: Our findings revealed direct and moderating effects of offline and online social support on suicidal ideation among youth under the pressure of different types of negative life events, as well as gender-specific patterns in these relationships.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".