Literature DB >> 667507

Classification of suicide attempters by cluster analysis.

E S Paykel, E Rassaby.   

Abstract

Cluster analytic procedures for classification were carried out on a sample of 236 suicide attempters. Rating variables concerned previous suicidal behaviour, details of the recent attempt and its motivation, mental state, and demographic characteristics. Results suggested the existence of three groups of attempters. The first comprised patients taking overdoses, on the whole showing less risk to life, less psychiatric disturbance, and more evidence of interpersonal rather than self-destructive motivation. The second groups, fewer in number, made severe attempts with more self-destructive motivation, by violent methods rather than overdose. The third and smallest group had a previous history of many attempts and gestures, made relatively mild attempts and were overtly hostile, engendering reciprocal hostility in the treating psychiatrist. These groups show some resemblance to those found in other studies.

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Year:  1978        PMID: 667507     DOI: 10.1192/bjp.133.1.45

Source DB:  PubMed          Journal:  Br J Psychiatry        ISSN: 0007-1250            Impact factor:   9.319


  6 in total

1.  Commentary: classification and cluster analysis.

Authors:  B S Everitt
Journal:  BMJ       Date:  1995-08-26

2.  [Significance of the first suicide attempt].

Authors:  M Choquet; F Facy; F Davidson; A Philippe
Journal:  Soc Psychiatry       Date:  1983

3.  Pathways to Late-Life Suicidal Behavior: Cluster Analysis and Predictive Validation of Suicidal Behavior in a Sample of Older Adults With Major Depression.

Authors:  Katalin Szanto; Hanga Galfalvy; Polina M Vanyukov; John G Keilp; Alexandre Y Dombrovski
Journal:  J Clin Psychiatry       Date:  2018 Mar/Apr       Impact factor: 4.384

4.  Death Ambivalence and Treatment Seeking: Suicidality in Opiate Addiction.

Authors:  Stacey C Conroy; James M Bjork
Journal:  Curr Treat Options Psychiatry       Date:  2018-07-09

5.  Appropriate interventions for the prevention and management of self-harm: a qualitative exploration of service-users' views.

Authors:  Megan Hume; Stephen Platt
Journal:  BMC Public Health       Date:  2007-01-19       Impact factor: 3.295

6.  Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain.

Authors:  Trung C Phan; Adrian Pranata; Joshua Farragher; Adam Bryant; Hung T Nguyen; Rifai Chai
Journal:  Sensors (Basel)       Date:  2022-09-04       Impact factor: 3.847

  6 in total

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