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Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours

BACKGROUND: Research on elder abuse has defined it as a multidimensional construct that encompasses a set of different abusive behaviours, victims, perpetrators and settings. The array of possible elder abuse configurations is difficult to capture. This study sought to identify victimization pattern...

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Autores principales: Santos, Ana João, Nunes, Baltazar, Kislaya, Irina, Gil, Ana Paula, Ribeiro, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480599/
https://www.ncbi.nlm.nih.gov/pubmed/31014265
http://dx.doi.org/10.1186/s12877-019-1111-5
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author Santos, Ana João
Nunes, Baltazar
Kislaya, Irina
Gil, Ana Paula
Ribeiro, Oscar
author_facet Santos, Ana João
Nunes, Baltazar
Kislaya, Irina
Gil, Ana Paula
Ribeiro, Oscar
author_sort Santos, Ana João
collection PubMed
description BACKGROUND: Research on elder abuse has defined it as a multidimensional construct that encompasses a set of different abusive behaviours, victims, perpetrators and settings. The array of possible elder abuse configurations is difficult to capture. This study sought to identify victimization patterns that represent distinct elder abuse configurations based on specific abusive behaviours and on the relationship with the perpetrator; it also sought to determine the association between these latent classes with victims’ characteristics. METHOD: Data comes from two elder abuse surveys: a representative sample of community-dwelling adults and a convenience sample of older adults reporting elder abuse to four state and NGOs institutions. Latent Class Analysis (LCA) was used to categorize victimization in the population-based (N = 245) and in the victims’ sample (N = 510) using 7 items measuring physical, psychological and financial abuse, and appointed perpetrators. Association tests were conducted to determine differences and similarities of victims’ characteristics between the different obtained classes. RESULTS: The LCA procedure identified six different latent classes of victimization experiences in each of the samples, which were statistically and plausibly distinct. In the population-based survey: verbal abuse by others (29%); psychological abuse from children/grandchildren (18%); overlooked by others (18%); stolen by others (15%); verbal Intimate Partner Violence (IPV) (14%) and physical and psychological IPV (6%). In the victims’ survey: physical abuse by children/grandchildren (29%); physical IPV (26%); psychological abuse by children/grandchildren (18%); polyvictimization by others (16%); physical abuse by others (6%) and physical and psychological IPV (4%). In the victims survey the 6 groups significantly differ in age, gender, civil status, living arrangements, perceived social support and functional status. CONCLUSIONS: The results support the possibility of the multidimensionality of elder abuse not being accounted by the “classical” abuse typologies. Elder abuse victims seeking help may represent a distinct group from that included in population-based prevalence studies. The appointed perpetrators may be the most meaningful and relevant aspect in distinguishing victimization experiences. Further research is needed to develop tailored interventions to specific elder abuse cases and enhance successful outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12877-019-1111-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-64805992019-05-01 Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours Santos, Ana João Nunes, Baltazar Kislaya, Irina Gil, Ana Paula Ribeiro, Oscar BMC Geriatr Research Article BACKGROUND: Research on elder abuse has defined it as a multidimensional construct that encompasses a set of different abusive behaviours, victims, perpetrators and settings. The array of possible elder abuse configurations is difficult to capture. This study sought to identify victimization patterns that represent distinct elder abuse configurations based on specific abusive behaviours and on the relationship with the perpetrator; it also sought to determine the association between these latent classes with victims’ characteristics. METHOD: Data comes from two elder abuse surveys: a representative sample of community-dwelling adults and a convenience sample of older adults reporting elder abuse to four state and NGOs institutions. Latent Class Analysis (LCA) was used to categorize victimization in the population-based (N = 245) and in the victims’ sample (N = 510) using 7 items measuring physical, psychological and financial abuse, and appointed perpetrators. Association tests were conducted to determine differences and similarities of victims’ characteristics between the different obtained classes. RESULTS: The LCA procedure identified six different latent classes of victimization experiences in each of the samples, which were statistically and plausibly distinct. In the population-based survey: verbal abuse by others (29%); psychological abuse from children/grandchildren (18%); overlooked by others (18%); stolen by others (15%); verbal Intimate Partner Violence (IPV) (14%) and physical and psychological IPV (6%). In the victims’ survey: physical abuse by children/grandchildren (29%); physical IPV (26%); psychological abuse by children/grandchildren (18%); polyvictimization by others (16%); physical abuse by others (6%) and physical and psychological IPV (4%). In the victims survey the 6 groups significantly differ in age, gender, civil status, living arrangements, perceived social support and functional status. CONCLUSIONS: The results support the possibility of the multidimensionality of elder abuse not being accounted by the “classical” abuse typologies. Elder abuse victims seeking help may represent a distinct group from that included in population-based prevalence studies. The appointed perpetrators may be the most meaningful and relevant aspect in distinguishing victimization experiences. Further research is needed to develop tailored interventions to specific elder abuse cases and enhance successful outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12877-019-1111-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-23 /pmc/articles/PMC6480599/ /pubmed/31014265 http://dx.doi.org/10.1186/s12877-019-1111-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Santos, Ana João
Nunes, Baltazar
Kislaya, Irina
Gil, Ana Paula
Ribeiro, Oscar
Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours
title Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours
title_full Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours
title_fullStr Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours
title_full_unstemmed Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours
title_short Elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours
title_sort elder abuse victimization patterns: latent class analysis using perpetrators and abusive behaviours
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480599/
https://www.ncbi.nlm.nih.gov/pubmed/31014265
http://dx.doi.org/10.1186/s12877-019-1111-5
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