Excess COVID-19 mortality risk in people with severe mental illness – comparing findings from two UK cohort studies
Bibliographic record
Abstract
Introduction: COVID-19-related physical isolation, fear and anxiety determined de novo mental illnesses, by potentially facilitating the emergence of social withdrawal Hikikomori-like traits (i.e., a severe social withdrawal condition), particularly among young people.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 the boredom and the loneliness dimensions.Methods: This study set a descriptive nationwide population-based cross-sectional online survey using a self-selection sampling strategy, created through the platform Google Form.The survey was disseminated through a multistep procedure: a) email invitation to healthcare professionals and their patients; b) social media channels (Facebook, Twitter and Instagram); c) mailing lists of universities, national medical associations and associations of stakeholders (e.g., associations of users/carers); and, d) other official websites (e.g., healthcare or welfare authorities websites).All data were collected anonymously and voluntarily between January, 2021 and Febraury, 2021.Participants are all university students currently registered (regular or irregular) in 2021 or just graduated in 2020, without any restriction by sex and age.Eligible participants were screened for Hikikomori traits by using Hikikomori Questionnaire (HQ-11), while they were assessed through Italian Loneliness Scale (ILS), Multidimensional State Boredom Scale (MSBS), Depression Anxiety Stress Scale (DASS-21) and Toronto Alexithymia Scale (TAS-20) during the timeframe January 2021-February 2021.All statistical analyses were performed using the software Statistical Package for Social Science (SPSS) version 25.0 for Windows (IBM SPSS Statistics, Chicago, IL, United States).Results: 1,148 respondents (767 women and 374 men, mean age: 23.2ÆSD¼2.8years old) were recruited.Females displayed a slightly higher mean age, compared to males (p¼0.009).Most participants were between ages 20 and 24 (n¼729; 63.9%).70.7% declared to have experienced psychological distress.HQ-11 average total score was 18.4ÆSD¼7.5 with statistically significant higher values in the males (p¼0.017) and amongst students studying Informatics, Mathematics/Physics/ Chemistry, Science of Communication and Engineering and in those who do not work while studying (p¼0.017).In particular, students of Informatics significantly reported higher HQ-11 compared to all other university courses (p¼0.021).Linear regression analysis found that ILS is a predictive factor of HQ-11 (R¼0.609;R2¼371; F(1)¼673.933;p<0.001).The HQ-11 positively correlated with ILS total score (r¼0.609), the subscale social isolation (r¼0.517), the subscale emotional loneliness (r¼0.441),MSBS total score (r¼0.415), the MSBS subscale disengagment (r¼0.395), the MSBS subscale higher arousal (r¼0.370), the MSBS inattention subscale (r¼0.319), the MSBS subscale low arousal (r¼0.542), the MSBS time perception subscale (r¼0.131),TAS-20 (r¼0.482),DASS-21 total score (r¼0.434),DASS-21 depression subscale (r¼0.497),DASS-21 anxiety subscale (r¼0,303), DASS-21 stress subscale (r¼0,365).Conclusion: 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.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".