COVID-19 anxiety predicts loneliness among university students: the mediating roles of mattering, fear of not mattering, and anti-mattering
Bibliographic record
Abstract
The current study evaluated the association between COVID-19 anxiety and loneliness among university students, as well as to investigate whether mattering, anti-mattering, and fear of not mediate this association. The study involved 450 university students from Canada, Russia, and Iran, consisting of 390 women and 60 men. Results of the correlational analysis, revealed that COVID-19 anxiety was positively correlated with loneliness (r = .48, p < .01), anti-mattering (r = .44, p < .01), and fear of not mattering (r = .46, p < .01), and negatively correlated with mattering (r = −0.20, p < .01). Conversely, mattering was negatively correlated with anti-mattering (r = −0.44, p < .01), and fear of not mattering (r = −0.23, p < .01). Regarding mediation analysis, the findings revealed that mattering, anti-mattering, and fear of not mattering mediated the association between COVID-19 anxiety and loneliness among university students. The results of the current study highlight the importance of enhancing individuals’ sense of mattering as a protective factor that can reduce the impact of psychological stress and anxiety associated with pandemic and the likelihood of engaging in maladaptive behaviors. This can prevent individuals from engaging in maladaptive behaviors, such as loneliness, addiction, and the use of negative coping strategies to deal with stressful events.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".