Various Types of Negative Life Events Among Youth Predict Suicidal Ideation: A Cross-Sectional Study Based on Gender Perspective
Bibliographic record
Abstract
We sought to investigate potential gender differences in various types of negative life events and suicidal ideation among Chinese high school and university students, as well as to analyze the interrelations between different types of negative life events and suicidal ideation among these young students. Participants were 2,018 high school and university students from northwestern China, who completed a demographics questionnaire and self-report measures of negative life events and suicidal ideation. Significant gender differences emerged in the types of negative life events reported and in students' degree of suicidal ideation. Within each gender group, different types of negative life events were predictive of the intensity of suicidal ideation. The present study provides evidence of the role of negative life events in predicting adolescents' and young adults' suicidal ideation regardless of their gender. However, gender differences did emerge in the specific type of negative life events that were predictive of suicidal ideation. For males, greater reports of punishment and adaptation had a significant positive impact on the intensity of their suicidal ideation. For females, greater reports of academic stress, personal loss, interpersonal relationships, and adaptation were all significantly and positively predictive of their suicidal ideation. Parents' marital status emerged as a significant indicator of suicidal ideation across genders, while age was significant negative predictor of suicidal ideation among females only.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".