Nonsuicidal self-injury among Chinese university students during the post-COVID-19 era: analysis of sex differences and the impact of gender role conflict
Bibliographic record
Abstract
Background: Global centers of epidemic prevention and control have entered a new stage of normalization, namely, the "post-COVID-19 era." During the post-COVID-19 era, which is characterized by the time period following that with the most serious medical consequences, the psychosocial consequences of the COVID-19 pandemic began to receive worldwide attention, especially the degree of psychological distress it caused. Aim: This study explored the differential impact of gender role conflict on Chinese university students' engagement in nonsuicidal self-injury (NSSI) as a function of biological sex following the global COVID-19 pandemic. Methods: = 21.3 years; 50.8% women) who completed online measures of demographic variables (including biological sex, gender role conflict, and NSSI engagement). Results: Women reported significantly more gender role conflicts than men did, while engagement in NSSI was significantly more prevalent among men than women. A total of 262 men reported engaging in at least one NSSI behavior, resulting in a prevalence rate of 33.25%. In comparison, a total of 106 individuals reported engaging in at least one NSSI behavior, resulting in a prevalence rate of 13.05% among women. Gender role conflict was found to significantly predict university students' NSSI engagement, regardless of biological sex. Conclusion: This is the first empirical study to identify sex differences in both gender role conflict and engagement in NSSI among university students in Northwestern China during the post-COVID-19 era. In addition, the present study is the first to demonstrate how gender role conflict predicts engagement in NSSI across sexes. These findings will inform the literature on gender role conflict and NSSI, particularly the close relationship between gender role conflict and engagement in NSSI among Chinese university students, and they emphasize the need for continued efforts to explore NSSI cross-culturally.
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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 | 0.001 |
| 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".