Boredom, loneliness and modern type depression in a cohort of Italian university students
Bibliographic record
Abstract
Introduction COVID-19-related physical isolation, fear and anxiety determined de novo mental illnesses, by potentially facilitating the emergence of Hikikomori traits (i.e., a severe social withdrawal condition). Objectives The present study aims at screening a cohort of university students for the Hikikomori traits and assessing a set of psychopathological determinants associated with Hikikomori, particularly boredom and loneliness dimensions. Methods A cross-sectional web-based survey was carried out by administering Hikikomori Questionnaire (HQ-11), Italian Loneliness Scale (ILS), Multidimensional State Boredom Scale (MSBS), Depression Anxiety Stress Scale (DASS-21) and Toronto Alexithymia Scale (TAS-20). Results 1,148 respondents (767 women and 374 men, mean age: 23.2±SD=2.8 years old) were recruited. 70.7% declared to have experienced psychological distress. HQ-11 average total score was 18.4±SD=7.5 with statistically significant higher values in the males (p=0.017) and amongst students studying Informatics, Mathematics/Physics/Chemistry, Science of Communication and Engineering. The HQ-11 positively correlated with ILS (r=0.609), MSBS (r=0.415), TAS-20 (r=0.482) and DASS-21 (r=0.434). Conclusions This study represents the first screening of the Hikikomori phenomenon in Italian university students. Hikikomori traits appear to be particularly represented in the Italian youth population and should be carefully investigated in future studies. Disclosure No significant 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".