The impact of perceived life stress and online social support on university students’ mental health during the post-COVID era in Northwestern China: gender-specific analysis
Bibliographic record
Abstract
BACKGROUND: Before the pandemic, research had already established the potential impact of perceived life stress and social support on the mental health status of Chinese students. However, in the Post-COVID Era, the specific mechanisms linking these variables and the distinct role of online social support remain relatively unexplored. METHODS: After the cessation of China's dynamic zeroing policy, a total of 1180 university students from Northwestern China participated in this study by completing a demographic questionnaire, as well as self-report measures assessing mental health, perceived life stress, and online social support. RESULTS: Approximately 25% of students exhibited psychological symptoms. When examining different categories of perceived life stress, males reported experiencing a significantly greater impact in terms of punishment and interpersonal relationships compared to females. Females experienced significantly higher levels of learning pressure compared to males. Specific types of perceived life stress were found to be significant predictors of students' mental health status. Moreover, online social support was identified as a significant moderator in the relationship between all types of perceived life stress and mental health, irrespective of gender. CONCLUSION: Our study findings unveiled two significant aspects: Firstly, the impact of perceived life stress on the mental health of students was identified as a risk factor. Secondly, the role of online social support emerged as a protective factor, particularly in the post-pandemic context. Additionally, gender-specific patterns were observed 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 | 0.001 |
| 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) | 0.002 | 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".