Unraveling the influence of offline and online social support on the connection between negative life events and suicidal ideation: a cross-sectional and gender-based examination among Chinese students amid the COVID-19 crisis
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate whether offline and online social support mediate the relationship between negative life events (NLEs) and suicidal ideation among Chinese students during the COVID-19 pandemic, with a focus on gender differences. METHODS: Using stratified sampling, 1,800 middle-high school and university students across China were surveyed. Structural equation modeling (SEM) with bias-corrected bootstrapping (5,000 samples) tested mediation effects, while multigroup SEM evaluated gender invariance. The Adolescent Self-Rating Life Events Checklist (ASLEC), Scale of Suicidal Ideation (SSI), and modified social support scales were administered. RESULTS: Offline social support significantly mediated NLEs’ impact on suicidal ideation: among males, for punishment (indirect effect = -0.007, 95% CI [-0.012, -0.003]) and adaptation (0.009, [0.004, 0.015]); among females, for learning pressure (0.008, [0.003, 0.013]), loss (0.013, [0.006, 0.021]), interpersonal relationships (0.024, [0.015, 0.034]), and adaptation (0.015, [0.008, 0.023]). Online social support showed no mediating effects for either gender. Multigroup SEM confirmed gender-invariant mediation structures (Δχ2 = 3.29, p = 0.17). CONCLUSION: Offline social support serves as a critical mediator of NLEs’ effects on suicidal ideation, with gender-specific pathways. Online support cannot substitute for tangible networks during crises. Findings emphasize the need for gender-tailored offline support interventions and longitudinal research to inform suicide prevention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".