Problematic Short Video Use and Anxiety in Chinese University Students: A Mediation Analysis of Leisure Boredom
Bibliographic record
Abstract
This study aims to explore the relationship between problematic short video use and anxiety among university students, as well as the mediating role of leisure boredom in this association. A questionnaire survey was conducted to collect data. Instruments included a self-developed demographic questionnaire, the Problematic Short Video Use Scale for College Students, the Leisure Boredom Scale, and an anxiety questionnaire. Among the 442 questionnaires collected, those with abnormal response times or patterned answering were excluded, resulting in 413 valid responses, yielding a valid response rate of 93.439%. SPSS software was used to organize and analyze the data, and the results showed that: (1) Problematic short video (PSV) use significantly and positively predicted students' levels of anxiety; (2) PSV use significantly and positively predicted levels of leisure boredom; (3) Leisure boredom significantly and positively predicted anxiety; and (4) Leisure boredom played a significant mediating role in the relationship between problematic short video use and anxiety. Specifically, problematic short video use increased feelings of leisure boredom, which in turn heightened anxiety levels. The mediating effect accounted for 59.15% of the total effect. These findings highlight the importance of addressing university students' short video use behaviors and their potential negative impact on leisure experiences, in order to reduce mental health risks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".