Modelo preditor de risco para violência doméstica contra a mulher
Ano de defesa: | 2020 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso embargado |
Idioma: | por |
Instituição de defesa: |
Universidade Federal da Paraíba
Brasil Ciências Exatas e da Natureza Programa de Pós-Graduação em Modelos de Decisão e Saúde UFPB |
Programa de Pós-Graduação: |
Não Informado pela instituição
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Departamento: |
Não Informado pela instituição
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País: |
Não Informado pela instituição
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Palavras-chave em Português: | |
Link de acesso: | https://repositorio.ufpb.br/jspui/handle/123456789/20226 |
Resumo: | Objective: To validate a risk predictor model to support specific care decision-making for women in situations of domestic violence. Method: This is a population-based applied methodological research. The research consisted of two distinct phases: the elaboration of a predictor model based on the Neural Network model having as input variables, the data extracted from the database of the study by Lucena (2015). The database contains variables related to the sociodemographic, epidemiological profile and the quality of life of women over 18 years of age, in addition to data on the measurement of the types of violence perpetrated by the intimate partner. For the operationalization of the second phase, mobile devices (tablet / cell phones) were used, where, through the sampling plan, 58 primary care professionals from the city of João Pessoa-PB were selected and instructed to participate in a pilot project, applying the predictor model in women seen at the service. The study met all the requirements of the ethics and research committee of the Paraíba State Health Department. Results: using the questionnaire that infers quality of life (WHOQOL-BREF) and the questionnaire that evaluates violence against women (WHO VAW STUDY), the significant variables were obtained through the neural network model and multiple logistic regression. Thus, the likelihood of a woman experiencing domestic violence was calculated. This numerical expression was transcribed to the VCMulher application software. The application was created to be used by primary care professionals, who are closer to women, in order to predict and identify victims of domestic violence. The VCMulher application achieved 83% approval by professionals. Regarding the risk of suffering violence, the data collected pointed out that of the 165 women participants, 98.8% have a medium to high risk of suffering violence. About 19% had a more than 90% chance of suffering domestic violence. Conclusion: The construction of the risk predictor model met the objective proposed in the study, presenting itself as a powerful instrument to identify risks / cases of domestic violence against women in the scope of primary care. It is necessary to invest in the qualification of health professionals to provide comprehensive care to women victims of violence. |
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Modelo preditor de risco para violência doméstica contra a mulherSaúde públicaViolência contra a mulherTomada de decisãoPublic healthViolence against womenDecision makingCNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVAObjective: To validate a risk predictor model to support specific care decision-making for women in situations of domestic violence. Method: This is a population-based applied methodological research. The research consisted of two distinct phases: the elaboration of a predictor model based on the Neural Network model having as input variables, the data extracted from the database of the study by Lucena (2015). The database contains variables related to the sociodemographic, epidemiological profile and the quality of life of women over 18 years of age, in addition to data on the measurement of the types of violence perpetrated by the intimate partner. For the operationalization of the second phase, mobile devices (tablet / cell phones) were used, where, through the sampling plan, 58 primary care professionals from the city of João Pessoa-PB were selected and instructed to participate in a pilot project, applying the predictor model in women seen at the service. The study met all the requirements of the ethics and research committee of the Paraíba State Health Department. Results: using the questionnaire that infers quality of life (WHOQOL-BREF) and the questionnaire that evaluates violence against women (WHO VAW STUDY), the significant variables were obtained through the neural network model and multiple logistic regression. Thus, the likelihood of a woman experiencing domestic violence was calculated. This numerical expression was transcribed to the VCMulher application software. The application was created to be used by primary care professionals, who are closer to women, in order to predict and identify victims of domestic violence. The VCMulher application achieved 83% approval by professionals. Regarding the risk of suffering violence, the data collected pointed out that of the 165 women participants, 98.8% have a medium to high risk of suffering violence. About 19% had a more than 90% chance of suffering domestic violence. Conclusion: The construction of the risk predictor model met the objective proposed in the study, presenting itself as a powerful instrument to identify risks / cases of domestic violence against women in the scope of primary care. It is necessary to invest in the qualification of health professionals to provide comprehensive care to women victims of violence.Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqObjetivo: Validar um modelo preditor de risco para o apoio à tomada de decisão específica de cuidado às mulheres em situação de violência doméstica. Método: Trata-se de uma pesquisa metodológica do tipo aplicada, de base populacional. A pesquisa foi constituída de duas fases distintas: a elaboração de um modelo preditor baseado no modelo de Rede Neurais tendo como variáveis de entrada, os dados extraídos do banco de dados do estudo de Lucena (2015). O banco de dados contém variáveis relacionadas ao perfil sociodemográfico, epidemiológico e a qualidade de vida de mulheres acima de 18 anos, além de dados acerca da mensuração dos tipos de violência perpetrada pelo parceiro íntimo. Para operacionalização da segunda fase, utilizou-se dispositivos móveis (tablete/celulares), onde por meio do plano amostral, 58 profissionais da atenção básica do município de João Pessoa-PB, foram selecionados e instruídos para a participação de um projeto piloto, aplicando o modelo preditor em mulheres atendidas no serviço. O estudo atendeu a todos os requisitos do comitê de ética e pesquisa da Secretaria de Saúde do Estado da Paraíba. Resultados: utilizando como base o questionário que infere qualidade de vida (WHOQOL-BREF) e o questionário que avalia a violência contra a mulher (WHO VAW STUDY), foram obtidas por meio do modelo de redes neurais e regressão logística múltipla as variáveis significativas. Dessa forma, foi calculada a probabilidade de uma mulher sofrer violência doméstica. Essa expressão numérica foi transcrita para o software do aplicativo VCMulher. O aplicativo foi criado para ser utilizado pelos profissionais da atenção básica, que estão mais próximas as mulheres, no intuito de prever e identificar as vítimas de violência doméstica. O aplicativo VCMulher conseguiu 83% de aprovação pelos profissionais. Em relação ao risco de sofre violência, os dados coletados apontaram que das 165 mulheres participantes 98,8% possuem risco médio a alto de sofre violência. Cerca de 19% apresentaram mais de 90% de chance de sofrer violência doméstica. Conclusão: A construção do modelo preditor de risco atendeu ao objetivo proposto no estudo, apresentando ser um potente instrumento para identificar riscos/casos de violência doméstica contra a mulher no âmbito da atenção básica. É necessário investir para a qualificação dos profissionais de saúde para que prestem atenção integral as mulheres vítimas de violência.Universidade Federal da ParaíbaBrasilCiências Exatas e da NaturezaPrograma de Pós-Graduação em Modelos de Decisão e SaúdeUFPBCoelho, Hemilio Fernandes Camposhttp://lattes.cnpq.br/2328238717105962Vianna, Rodrigo Pinheiro de Toledohttp://lattes.cnpq.br/3915051035089861Deininger, Layza de Souza Chaves2021-06-28T18:55:44Z2021-02-202021-06-28T18:55:44Z2020-02-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesishttps://repositorio.ufpb.br/jspui/handle/123456789/20226porhttp://creativecommons.org/licenses/by-nd/3.0/br/info:eu-repo/semantics/embargoedAccessreponame:Biblioteca Digital de Teses e Dissertações da UFPBinstname:Universidade Federal da Paraíba (UFPB)instacron:UFPB2021-06-29T06:39:00Zoai:repositorio.ufpb.br:123456789/20226Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufpb.br/PUBhttp://tede.biblioteca.ufpb.br:8080/oai/requestdiretoria@ufpb.br|| diretoria@ufpb.bropendoar:2021-06-29T06:39Biblioteca Digital de Teses e Dissertações da UFPB - Universidade Federal da Paraíba (UFPB)false |
dc.title.none.fl_str_mv |
Modelo preditor de risco para violência doméstica contra a mulher |
title |
Modelo preditor de risco para violência doméstica contra a mulher |
spellingShingle |
Modelo preditor de risco para violência doméstica contra a mulher Deininger, Layza de Souza Chaves Saúde pública Violência contra a mulher Tomada de decisão Public health Violence against women Decision making CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA |
title_short |
Modelo preditor de risco para violência doméstica contra a mulher |
title_full |
Modelo preditor de risco para violência doméstica contra a mulher |
title_fullStr |
Modelo preditor de risco para violência doméstica contra a mulher |
title_full_unstemmed |
Modelo preditor de risco para violência doméstica contra a mulher |
title_sort |
Modelo preditor de risco para violência doméstica contra a mulher |
author |
Deininger, Layza de Souza Chaves |
author_facet |
Deininger, Layza de Souza Chaves |
author_role |
author |
dc.contributor.none.fl_str_mv |
Coelho, Hemilio Fernandes Campos http://lattes.cnpq.br/2328238717105962 Vianna, Rodrigo Pinheiro de Toledo http://lattes.cnpq.br/3915051035089861 |
dc.contributor.author.fl_str_mv |
Deininger, Layza de Souza Chaves |
dc.subject.por.fl_str_mv |
Saúde pública Violência contra a mulher Tomada de decisão Public health Violence against women Decision making CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA |
topic |
Saúde pública Violência contra a mulher Tomada de decisão Public health Violence against women Decision making CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA |
description |
Objective: To validate a risk predictor model to support specific care decision-making for women in situations of domestic violence. Method: This is a population-based applied methodological research. The research consisted of two distinct phases: the elaboration of a predictor model based on the Neural Network model having as input variables, the data extracted from the database of the study by Lucena (2015). The database contains variables related to the sociodemographic, epidemiological profile and the quality of life of women over 18 years of age, in addition to data on the measurement of the types of violence perpetrated by the intimate partner. For the operationalization of the second phase, mobile devices (tablet / cell phones) were used, where, through the sampling plan, 58 primary care professionals from the city of João Pessoa-PB were selected and instructed to participate in a pilot project, applying the predictor model in women seen at the service. The study met all the requirements of the ethics and research committee of the Paraíba State Health Department. Results: using the questionnaire that infers quality of life (WHOQOL-BREF) and the questionnaire that evaluates violence against women (WHO VAW STUDY), the significant variables were obtained through the neural network model and multiple logistic regression. Thus, the likelihood of a woman experiencing domestic violence was calculated. This numerical expression was transcribed to the VCMulher application software. The application was created to be used by primary care professionals, who are closer to women, in order to predict and identify victims of domestic violence. The VCMulher application achieved 83% approval by professionals. Regarding the risk of suffering violence, the data collected pointed out that of the 165 women participants, 98.8% have a medium to high risk of suffering violence. About 19% had a more than 90% chance of suffering domestic violence. Conclusion: The construction of the risk predictor model met the objective proposed in the study, presenting itself as a powerful instrument to identify risks / cases of domestic violence against women in the scope of primary care. It is necessary to invest in the qualification of health professionals to provide comprehensive care to women victims of violence. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-20 2021-06-28T18:55:44Z 2021-02-20 2021-06-28T18:55:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufpb.br/jspui/handle/123456789/20226 |
url |
https://repositorio.ufpb.br/jspui/handle/123456789/20226 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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http://creativecommons.org/licenses/by-nd/3.0/br/ info:eu-repo/semantics/embargoedAccess |
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http://creativecommons.org/licenses/by-nd/3.0/br/ |
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embargoedAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal da Paraíba Brasil Ciências Exatas e da Natureza Programa de Pós-Graduação em Modelos de Decisão e Saúde UFPB |
publisher.none.fl_str_mv |
Universidade Federal da Paraíba Brasil Ciências Exatas e da Natureza Programa de Pós-Graduação em Modelos de Decisão e Saúde UFPB |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UFPB instname:Universidade Federal da Paraíba (UFPB) instacron:UFPB |
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Biblioteca Digital de Teses e Dissertações da UFPB |
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Biblioteca Digital de Teses e Dissertações da UFPB - Universidade Federal da Paraíba (UFPB) |
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diretoria@ufpb.br|| diretoria@ufpb.br |
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