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 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 | 0.001 |
| 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".