The Mediating Effect of Stress between Extracurricular Activities and Suicidal Ideation in Chinese College Students
Bibliographic record
Abstract
Objective: The objective of this study was to explore the relationship between extracurricular activities, stress, and suicidal ideation and to examine the mediating effect of stress between extracurricular activities and suicidal ideation in Chinese college students. Method: A total of 6446 college students were surveyed with a web-based online data collection system using the self-made demographic questionnaire, Suicidal Behaviors Questionnaire—Revised (SBQ-R), and the 21-Item Depression Anxiety Stress Scales (DASS-21). SPSS 24.0 was used for descriptive statistics and correlation analysis, and the bootstrap method in the process procedure for SPSS Version 3.4.1 was used to construct the mediating effect model. Results: Gender, school grades, living area, and family income status were influencing factors for suicidal ideation, stress, and extracurricular activities. Extracurricular activities were negatively correlated with stress (r = −0.083, p < 0.001) and suicidal ideation (r = −0.039, p < 0.01). Extracurricular activities had no direct predictive effect on college students’ suicidal ideation (c = −0.198, CI: −0.418, 0.023), while stress had a mediating effect between extracurricular activities and suicidal ideation; the indirect mediating effect was 0.159. Conclusions: Extracurricular activities indirectly predict college students’ suicidal ideation through stress. A variety of extracurricular activities can decrease the stress and suicidal ideation of college students and benefit their mental health.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".