Excess COVID-19 mortality risk in people with severe mental illness – comparing findings from two UK cohort studies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».