The relationship between specific problematic internet use and hope: academic exhaustion as mediator and mattering as moderator among Chinese university students
Bibliographic record
Abstract
Problematic social media use (PSMU) and problematic gaming (PG) among university students as specific types of problematic internet use (PIU) have become a growing concern. PSMU and PG may lead to negative outcomes such as academic exhaustion and diminished hope. However, previous studies have not simultaneously considered the associations among these variables from the perspective of Stressor-Strain-Outcome model. Furthermore, the concept of 'mattering'-the feeling of being valued and important to others and 'fear of not mattering' in this dynamic is notably under-investigated. The present study aimed to examine the associations among these variables and evaluated whether mattering profiles moderated the associations involving PIU among university students.A survey was conducted among 3,035 university students in China, with an average age of 19.24 years (SD = 1.83). The sample included 52% males and 48% females from 19 different universities. The Bergen Social Media Addiction Scale, the Internet Gaming Disorder Scale-Short Form, the General Mattering Scale, the Fear of Not Mattering Inventory, the Maslach Burnout Inventory-Student Survey, and the Dispositional Hope Scale were utilized to evaluate PSMU, PG, general mattering, fear of not mattering, academic exhaustion, and hope, respectively. Furthermore, latent profile analysis was used to categorize students into distinct mattering profiles based on measures of general mattering and fear of not mattering to others.Correlational analyses indicated that PSMU and PG were associated with greater academic exhaustion, reduced hope, and higher levels of fear of not mattering. Mediation analysis identified academic exhaustion as a mediator in the relationships between PSMU and hope, as well as between PG and hope. Profile analyses identified a group of students distinguished by exceptionally low levels of general mattering. Mattering profiles acted as moderators of the associations between PG and academic exhaustion, and between academic exhaustion and hope.PG negatively affected students' hope through academic exhaustion, while different mattering profiles had diverse associations. Customized intervention strategies focused on boosting hope and feelings of mattering, and reducing fears of not mattering are needed to reduce vulnerability to PG and PSMU.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".