Measuring the level of social support using latent class analysis
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFBA |
Texto Completo: | http://repositorio.ufba.br/ri/handle/ri/19161 |
Resumo: | Different instruments have been used to measure social support in epidemiological studies of which the most widely used is the Medical Outcomes Study Social Support Scale (SSSMOS). However, these studies lack measures of the level of social support on health risks. We used latent class analysis (LCA) to distinguish subgroups with different levels of perceived social support and tested the consistency of these subgroups by their associations with the prevalence of Common Mental Disorders (CMD). This is a cross-sectional study of 1013 mothers living in the city of Salvador, Brazil in which psychosocial data were collected through home visits using the SSS-MOS and the Self Reporting questionnaire-20. For each dimension of social support analysed here, we selected models with two classes using LCA. Multivariate logistic regression models were used to estimate the association between participants’ perceived social support and the prevalence of CMD to verify the consistency of the groups defined by LCA. There was a clear difference in the reporting of perceived social support between those classified as high or low using LCA. The probability of perceiving several types of social support was lower in the subgroup classified as low level of social support (13.7–59.8%), and it was much higher in the group classified as high level of social support (84.3–98%). A greater prevalence of CMD was found among mothers with lower levels of social support. LCA seems to be a useful tool to improve measurement of perceived social support by separation into two levels in which the lower level is associated with an increased prevalence of CMD. |
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Santos, Letícia Marques dosAmorim, Leila Denise Alves FerreiraSantos, Darci Neves dosBarreto, Mauricio Lima2016-05-11T14:48:34Z2015-031096-0317http://repositorio.ufba.br/ri/handle/ri/19161v.50, n.2015, p.139–146Different instruments have been used to measure social support in epidemiological studies of which the most widely used is the Medical Outcomes Study Social Support Scale (SSSMOS). However, these studies lack measures of the level of social support on health risks. We used latent class analysis (LCA) to distinguish subgroups with different levels of perceived social support and tested the consistency of these subgroups by their associations with the prevalence of Common Mental Disorders (CMD). This is a cross-sectional study of 1013 mothers living in the city of Salvador, Brazil in which psychosocial data were collected through home visits using the SSS-MOS and the Self Reporting questionnaire-20. For each dimension of social support analysed here, we selected models with two classes using LCA. Multivariate logistic regression models were used to estimate the association between participants’ perceived social support and the prevalence of CMD to verify the consistency of the groups defined by LCA. There was a clear difference in the reporting of perceived social support between those classified as high or low using LCA. The probability of perceiving several types of social support was lower in the subgroup classified as low level of social support (13.7–59.8%), and it was much higher in the group classified as high level of social support (84.3–98%). A greater prevalence of CMD was found among mothers with lower levels of social support. LCA seems to be a useful tool to improve measurement of perceived social support by separation into two levels in which the lower level is associated with an increased prevalence of CMD.Submitted by Maria Creuza Silva (mariakreuza@yahoo.com.br) on 2016-05-11T14:48:34Z No. of bitstreams: 1 Art Per Estrang Darci N. Santos. 2015.pdf: 236760 bytes, checksum: 76762c9dac317287d4e1ae82e2ef8b9e (MD5)Made available in DSpace on 2016-05-11T14:48:34Z (GMT). No. of bitstreams: 1 Art Per Estrang Darci N. Santos. 2015.pdf: 236760 bytes, checksum: 76762c9dac317287d4e1ae82e2ef8b9e (MD5) Previous issue date: 2015-03New YorkAcademic Presshttp://ac.els-cdn.com/S0049089X14002051/1-s2.0-S0049089X14002051-main.pdf?_tid=aa9f5b2a-1786-11e6-94cf-00000aacb35f&acdnat=1462977963_70836c930f7d1ebb5a67d8ea08dcde28reponame:Repositório Institucional da UFBAinstname:Universidade Federal da Bahia (UFBA)instacron:UFBALatent class analysisMental healthSSS-MOSSocial supportMeasuring the level of social support using latent class analysisSoc. Sci. Resinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10000-01-01info:eu-repo/semantics/openAccessengORIGINALArt Per Estrang Darci N. Santos. 2015.pdfArt Per Estrang Darci N. 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dc.title.pt_BR.fl_str_mv |
Measuring the level of social support using latent class analysis |
dc.title.alternative.pt_BR.fl_str_mv |
Soc. Sci. Res |
title |
Measuring the level of social support using latent class analysis |
spellingShingle |
Measuring the level of social support using latent class analysis Santos, Letícia Marques dos Latent class analysis Mental health SSS-MOS Social support |
title_short |
Measuring the level of social support using latent class analysis |
title_full |
Measuring the level of social support using latent class analysis |
title_fullStr |
Measuring the level of social support using latent class analysis |
title_full_unstemmed |
Measuring the level of social support using latent class analysis |
title_sort |
Measuring the level of social support using latent class analysis |
author |
Santos, Letícia Marques dos |
author_facet |
Santos, Letícia Marques dos Amorim, Leila Denise Alves Ferreira Santos, Darci Neves dos Barreto, Mauricio Lima |
author_role |
author |
author2 |
Amorim, Leila Denise Alves Ferreira Santos, Darci Neves dos Barreto, Mauricio Lima |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Santos, Letícia Marques dos Amorim, Leila Denise Alves Ferreira Santos, Darci Neves dos Barreto, Mauricio Lima |
dc.subject.por.fl_str_mv |
Latent class analysis Mental health SSS-MOS Social support |
topic |
Latent class analysis Mental health SSS-MOS Social support |
description |
Different instruments have been used to measure social support in epidemiological studies of which the most widely used is the Medical Outcomes Study Social Support Scale (SSSMOS). However, these studies lack measures of the level of social support on health risks. We used latent class analysis (LCA) to distinguish subgroups with different levels of perceived social support and tested the consistency of these subgroups by their associations with the prevalence of Common Mental Disorders (CMD). This is a cross-sectional study of 1013 mothers living in the city of Salvador, Brazil in which psychosocial data were collected through home visits using the SSS-MOS and the Self Reporting questionnaire-20. For each dimension of social support analysed here, we selected models with two classes using LCA. Multivariate logistic regression models were used to estimate the association between participants’ perceived social support and the prevalence of CMD to verify the consistency of the groups defined by LCA. There was a clear difference in the reporting of perceived social support between those classified as high or low using LCA. The probability of perceiving several types of social support was lower in the subgroup classified as low level of social support (13.7–59.8%), and it was much higher in the group classified as high level of social support (84.3–98%). A greater prevalence of CMD was found among mothers with lower levels of social support. LCA seems to be a useful tool to improve measurement of perceived social support by separation into two levels in which the lower level is associated with an increased prevalence of CMD. |
publishDate |
2015 |
dc.date.issued.fl_str_mv |
2015-03 |
dc.date.accessioned.fl_str_mv |
2016-05-11T14:48:34Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufba.br/ri/handle/ri/19161 |
dc.identifier.issn.none.fl_str_mv |
1096-0317 |
dc.identifier.number.pt_BR.fl_str_mv |
v.50, n.2015, p.139–146 |
identifier_str_mv |
1096-0317 v.50, n.2015, p.139–146 |
url |
http://repositorio.ufba.br/ri/handle/ri/19161 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Academic Press |
publisher.none.fl_str_mv |
Academic Press |
dc.source.pt_BR.fl_str_mv |
http://ac.els-cdn.com/S0049089X14002051/1-s2.0-S0049089X14002051-main.pdf?_tid=aa9f5b2a-1786-11e6-94cf-00000aacb35f&acdnat=1462977963_70836c930f7d1ebb5a67d8ea08dcde28 |
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reponame:Repositório Institucional da UFBA instname:Universidade Federal da Bahia (UFBA) instacron:UFBA |
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Universidade Federal da Bahia (UFBA) |
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UFBA |
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Repositório Institucional da UFBA |
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Repositório Institucional da UFBA |
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