Evidence map›Paper›PMID 36891593›Full record

ArticlePsychiatry investigation2023

A Latent Class Analysis of Suicidal Behaviors in Adolescents.

Jong-Sun Lee, Hoin Kwon, Jungkyu Park, Hyun Ju Hong, Yong-Sil Kweon

Abstract read
In one paragraph

Article in Psychiatry investigation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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.

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2 · The registry

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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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Self-Esteem and Risk Behaviours in Adolescents: A Systematic Review.Behavioral sciences (Basel, Switzerland) · 2024
    Review
  5. 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

5 authors.

Jong-Sun LeeDepartment of Psychology, Kangwon National University, Chuncheon, Republic of Korea.
Hoin KwonDepartment of Counseling Psychology, Jeonju University, Jeonju, Republic of Korea.
Jungkyu ParkDepartment of Psychology, Kyungpook National University, Daegu, Republic of Korea.
Hyun Ju HongSuicide and School Mental Health Institute, Hallym University, Anyang, Republic of Korea.
Yong-Sil KweonSuicide and School Mental Health Institute, Hallym University, Anyang, Republic of Korea.

Funding

Ministry of EducationNational Research Foundation of Korea 2018S1A5B8A02081988
6 · The paper itself

Abstract

objectiveThe purpose of the present study was to identify adolescents' suicide subgroups using five indicators (depression, anxiety, suicide ideation, and planned and attempted suicide), and explore the distinctive features of each subgroup.

methodsThis study included 2,258 teenagers from four schools. Both adolescents and their parents, who voluntarily agreed to participate in the study, completed a series of self-reported questionnaires on depression, anxiety, suicide, self-harm, self-esteem, impulsivity, childhood maltreatment, and deviant behaviors. The data were analyzed using latent class analysis, a person-centered method.

resultsFour classes were detected: "high risk for suicide without distress," "high risk for suicide with distress," "low risk for suicide with distress," and "healthy." The "high risk for suicide with distress" class was the most severe on all evaluated psychosocial risk factors, namely, impulsivity, low self-esteem, self-harming behaviours, deviant behaviour problems, and childhood maltreatment, followed by "high risk for suicide without distress."

conclusionThis study identified two high risk subgroups for adolescent' suicidality, "high risk for suicide with or without distress." Both high risk subgroups for suicide showed higher scores for all psychosocial risk factors than low risk subgroups for suicide. Our findings suggest that special attention needs to be paid to the latent class "high risk for suicide without distress," as this group's "cry for help" might be relatively difficult to detect. Specific interventions for each group (e.g., distress safety plans for "suicidal potential with or without emotional distress") need to be developed and implemented.

Indexed as

AdolescenceAnxietyDepressionLatent class analysisNon-suicidal self injurySuicide

Identifiers

PMID36891593
PMCPMC9996140

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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.