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A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation
BACKGROUND: The aim of this study was to investigate and model the interactions between a range of risk and protective factors for suicidal ideation using general population data collected during the critical phase of the COVID-19 pandemic. METHODS: Bayesian network analyses were applied to cross-se...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014716/ https://www.ncbi.nlm.nih.gov/pubmed/36935710 http://dx.doi.org/10.3389/fpubh.2023.1010264 |
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author | Delgadillo, Jaime Budimir, Sanja Barkham, Michael Humer, Elke Pieh, Christoph Probst, Thomas |
author_facet | Delgadillo, Jaime Budimir, Sanja Barkham, Michael Humer, Elke Pieh, Christoph Probst, Thomas |
author_sort | Delgadillo, Jaime |
collection | PubMed |
description | BACKGROUND: The aim of this study was to investigate and model the interactions between a range of risk and protective factors for suicidal ideation using general population data collected during the critical phase of the COVID-19 pandemic. METHODS: Bayesian network analyses were applied to cross-sectional data collected 1 month after the COVID-19 lockdown measures were implemented in Austria and the United Kingdom. In nationally representative samples (n = 1,005 Austria; n = 1,006 UK), sociodemographic features and a multi-domain battery of health, wellbeing and quality of life (QOL) measures were completed. Predictive accuracy was examined using the area under the curve (AUC) within-sample (country) and out-of-sample. RESULTS: The AUC of the Bayesian network models were ≥ 0.84 within-sample and ≥0.79 out-of-sample, explaining close to 50% of variability in suicidal ideation. In total, 15 interrelated risk and protective factors were identified. Seven of these factors were replicated in both countries: depressive symptoms, loneliness, anxiety symptoms, self-efficacy, resilience, QOL physical health, and QOL living environment. CONCLUSIONS: Bayesian network models had high predictive accuracy. Several psychosocial risk and protective factors have complex interrelationships that influence suicidal ideation. It is possible to predict suicidal risk with high accuracy using this information. |
format | Online Article Text |
id | pubmed-10014716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100147162023-03-16 A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation Delgadillo, Jaime Budimir, Sanja Barkham, Michael Humer, Elke Pieh, Christoph Probst, Thomas Front Public Health Public Health BACKGROUND: The aim of this study was to investigate and model the interactions between a range of risk and protective factors for suicidal ideation using general population data collected during the critical phase of the COVID-19 pandemic. METHODS: Bayesian network analyses were applied to cross-sectional data collected 1 month after the COVID-19 lockdown measures were implemented in Austria and the United Kingdom. In nationally representative samples (n = 1,005 Austria; n = 1,006 UK), sociodemographic features and a multi-domain battery of health, wellbeing and quality of life (QOL) measures were completed. Predictive accuracy was examined using the area under the curve (AUC) within-sample (country) and out-of-sample. RESULTS: The AUC of the Bayesian network models were ≥ 0.84 within-sample and ≥0.79 out-of-sample, explaining close to 50% of variability in suicidal ideation. In total, 15 interrelated risk and protective factors were identified. Seven of these factors were replicated in both countries: depressive symptoms, loneliness, anxiety symptoms, self-efficacy, resilience, QOL physical health, and QOL living environment. CONCLUSIONS: Bayesian network models had high predictive accuracy. Several psychosocial risk and protective factors have complex interrelationships that influence suicidal ideation. It is possible to predict suicidal risk with high accuracy using this information. Frontiers Media S.A. 2023-03-01 /pmc/articles/PMC10014716/ /pubmed/36935710 http://dx.doi.org/10.3389/fpubh.2023.1010264 Text en Copyright © 2023 Delgadillo, Budimir, Barkham, Humer, Pieh and Probst. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Delgadillo, Jaime Budimir, Sanja Barkham, Michael Humer, Elke Pieh, Christoph Probst, Thomas A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation |
title | A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation |
title_full | A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation |
title_fullStr | A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation |
title_full_unstemmed | A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation |
title_short | A Bayesian network analysis of psychosocial risk and protective factors for suicidal ideation |
title_sort | bayesian network analysis of psychosocial risk and protective factors for suicidal ideation |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014716/ https://www.ncbi.nlm.nih.gov/pubmed/36935710 http://dx.doi.org/10.3389/fpubh.2023.1010264 |
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