Evidence map›Paper›PMID 39814751›Full record

ArticleScientific reports2025

Self-isolation of adolescents after Covid-19 pandemic between social withdrawal and Hikikomori risk in Italy.

Loredana Cerbara, Giulia Ciancimino, Gianni Corsetti, Antonio Tintori

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Case Report: When pain is shown and silenced. adolescents looking for a voice.Frontiers in child and adolescent psychiatry · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Loredana CerbaraInstitute for Research on Population and Social Policies, National Research Council of Italy, 00185, Rome, Italy.
Giulia CianciminoInstitute for Research on Population and Social Policies, National Research Council of Italy, 00185, Rome, Italy. giulia.ciancimino@irpps.cnr.it.
Gianni CorsettiItalian National Institute of Statistics, 00184, Rome, Italy.
Antonio TintoriInstitute for Research on Population and Social Policies, National Research Council of Italy, 00185, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social withdrawal is a widespread phenomenon among adolescents, presenting significant challenges in understanding its aetiology and dynamics. This study, drawing on data from two cross-sectional surveys conducted in 2019 and 2022 on students in public upper secondary schools, investigates the trend of self-isolation among Italian adolescents before and after the COVID-19 pandemic. The two nationally representative samples comprise 3273 and 4288 participants, respectively, with 46.3% and 41.2% identifying as female, ranging in age from 14 to 19 years. Using Multiple Correspondence Analysis (MCA) and Hierarchical Cluster Analysis (HCA), three distinct profiles of social withdrawal were identified among adolescents: "Social Butterflies", "Friendship-Centric", and "Lone Wolves". Notably, the discovery of a subgroup within the Lone Wolves, composed of adolescents who never meet their friends and whose number has doubled post-pandemic, highlights the chronic nature of the phenomenon that demands urgent intervention. These findings shed light on the increase of self-isolation in Italy, showing the interplay of socio-demographic, psychological, and sociological factors underlying this phenomenon.

Indexed as

COVID-19Social IsolationAdolescentCross-Sectional StudiesFemaleHumansItalyMalePandemicsSARS-CoV-2Surveys and QuestionnairesYoung Adult

Identifiers

PMID39814751
PMCPMC11735792

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.