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Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women

BACKGROUND: We assumed that perimenopausal women’s sense of quality of health (SQH) is a subjective evaluation of their psycho-physical health, and comprises three dimensions: sense of quality of life, menopausal symptoms, and the level of positive and negative affect. PURPOSE: The aim of the study...

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Autores principales: Włodarczyk, Małgorzata, Dolińska-Zygmunt, Grażyna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6317781/
https://www.ncbi.nlm.nih.gov/pubmed/30605472
http://dx.doi.org/10.1371/journal.pone.0200129
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author Włodarczyk, Małgorzata
Dolińska-Zygmunt, Grażyna
author_facet Włodarczyk, Małgorzata
Dolińska-Zygmunt, Grażyna
author_sort Włodarczyk, Małgorzata
collection PubMed
description BACKGROUND: We assumed that perimenopausal women’s sense of quality of health (SQH) is a subjective evaluation of their psycho-physical health, and comprises three dimensions: sense of quality of life, menopausal symptoms, and the level of positive and negative affect. PURPOSE: The aim of the study was to: 1) test a model about SQH, and 2) explore the role of personality traits, self-esteem, body self, and self-stereotype as predictors of SQH. METHODS: The sample included 201 women aged between 45 and 55 (50.11±3.07). Participants filled out the Rosenberg Self-Esteem Scale, the Personality Inventory based on the Big Five Factor Model, the Body Self Questionnaire, and a survey querying perimenopausal women’s self-stereotype. To determine the individual SQH dimensions we used the Sense of Quality of Life Questionnaire, the Menopause Symptom List, and the Positive and Negative Affect Schedule. To verify the assumptions of the SQH model and look for SQH predictors we conducted a neural networks analysis with structure optimization via genetic algorithms (a multivariate analysis). RESULTS: The SQH model was verified in the course of several neural networks analyses with structure optimization via genetic algorithms (R = 0.849, R(2) = 0.723, F = 133,232, p < 0.01). Moreover, we confirmed that SQH comprised three dimensions: quality of life, menopausal symptoms, and affect. SQH and menopausal symptoms were correlated. Similarly, positive and negative affect modified the women’s global sense of quality of life. SQH predictors included: personality traits, self-esteem, the body-self, and menopausal woman’s self-stereotype. CONCLUSION: In practical terms, our findings may help raise awareness among women and medical practitioners, calling for a holistic approach to the health of menopausal women. Our findings may also facilitate the creation of both prevention and therapeutic programs for women transitioning through menopause, for example, cognitive-behavioral therapy.
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spelling pubmed-63177812019-01-19 Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women Włodarczyk, Małgorzata Dolińska-Zygmunt, Grażyna PLoS One Research Article BACKGROUND: We assumed that perimenopausal women’s sense of quality of health (SQH) is a subjective evaluation of their psycho-physical health, and comprises three dimensions: sense of quality of life, menopausal symptoms, and the level of positive and negative affect. PURPOSE: The aim of the study was to: 1) test a model about SQH, and 2) explore the role of personality traits, self-esteem, body self, and self-stereotype as predictors of SQH. METHODS: The sample included 201 women aged between 45 and 55 (50.11±3.07). Participants filled out the Rosenberg Self-Esteem Scale, the Personality Inventory based on the Big Five Factor Model, the Body Self Questionnaire, and a survey querying perimenopausal women’s self-stereotype. To determine the individual SQH dimensions we used the Sense of Quality of Life Questionnaire, the Menopause Symptom List, and the Positive and Negative Affect Schedule. To verify the assumptions of the SQH model and look for SQH predictors we conducted a neural networks analysis with structure optimization via genetic algorithms (a multivariate analysis). RESULTS: The SQH model was verified in the course of several neural networks analyses with structure optimization via genetic algorithms (R = 0.849, R(2) = 0.723, F = 133,232, p < 0.01). Moreover, we confirmed that SQH comprised three dimensions: quality of life, menopausal symptoms, and affect. SQH and menopausal symptoms were correlated. Similarly, positive and negative affect modified the women’s global sense of quality of life. SQH predictors included: personality traits, self-esteem, the body-self, and menopausal woman’s self-stereotype. CONCLUSION: In practical terms, our findings may help raise awareness among women and medical practitioners, calling for a holistic approach to the health of menopausal women. Our findings may also facilitate the creation of both prevention and therapeutic programs for women transitioning through menopause, for example, cognitive-behavioral therapy. Public Library of Science 2019-01-03 /pmc/articles/PMC6317781/ /pubmed/30605472 http://dx.doi.org/10.1371/journal.pone.0200129 Text en © 2019 Włodarczyk, Dolińska-Zygmunt http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Włodarczyk, Małgorzata
Dolińska-Zygmunt, Grażyna
Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women
title Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women
title_full Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women
title_fullStr Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women
title_full_unstemmed Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women
title_short Searching for predictors of sense of quality of health: A study using neural networks on a sample of perimenopausal women
title_sort searching for predictors of sense of quality of health: a study using neural networks on a sample of perimenopausal women
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6317781/
https://www.ncbi.nlm.nih.gov/pubmed/30605472
http://dx.doi.org/10.1371/journal.pone.0200129
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