Y oung adults in Neither in Employment nor in Education and Training (NEET) are at high risk of adverse health outcome, in particular of mental health problems. The aim of this study is to identify the symptomatological profiles of young Italian NEETs. The data set in question consists of 150 Italian respondents to the Adult Self Report (ASR 18-59) survey for assessing the mental health problems. A two-step unsupervised learning approach that involves fuzzy multiple correspondences analysis and clustering is applied to identify different symptomatological profiles of NEETs-related problems. The obtained results are compared to a principal component analysis-based approach. Finally, clinical implications in psychological practices are discussed.

Analysis of young people neither in employment nor in education and training: A fuzzy mca based approach

Parola Anna
;
Iodice D'Enza Alfonso
2021

Abstract

Y oung adults in Neither in Employment nor in Education and Training (NEET) are at high risk of adverse health outcome, in particular of mental health problems. The aim of this study is to identify the symptomatological profiles of young Italian NEETs. The data set in question consists of 150 Italian respondents to the Adult Self Report (ASR 18-59) survey for assessing the mental health problems. A two-step unsupervised learning approach that involves fuzzy multiple correspondences analysis and clustering is applied to identify different symptomatological profiles of NEETs-related problems. The obtained results are compared to a principal component analysis-based approach. Finally, clinical implications in psychological practices are discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/855197
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