To prevent gender violence it was developed the protocol called Police Risk Assessment (VPR) for use by professionals of the State Security Forces. This protocol is the core of VioGen System of the Spanish Interior Ministry and which applies to regulation in all reported gender violence situations. To assess the predictive effectiveness of the VPR, a prospective longitudinal study was performed followed for 3 and 6 months of 407 women who reported being victims of violence from their partner or former partner. The results obtained by logistic regression analysis provide an AUC = 0.71 for time intervals at risk for three months (P < .003), with an odds ratio of 6.58 (95% CI: 1.899-22.835). VPR sensitivity was 85% and specificity was 53.7%. The results indicate that the VPR shows good predictive ability and suitable psychometric characteristics for the task for which it was designed. Published by Elsevier Espana, S.L.U. on behalf of Colegio Oficial de Psicologos de Madrid.
机构:
Portland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USAPortland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USA
Henning, Kris
Campbell, Christopher M.
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Portland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USAPortland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USA
Campbell, Christopher M.
Stewart, Gregory
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Portland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USAPortland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USA
Stewart, Gregory
Johnson, Jennifer
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Portland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USAPortland State Univ, Dept Criminol & Criminal Justice, POB 751, Portland, OR 97201 USA
机构:
Univ Autonoma Madrid, Escuela Politecn Super, Madrid, Spain
Univ Carlos III Madrid, Santander Big Data Inst UC3M, Madrid, SpainUniv Autonoma Madrid, Escuela Politecn Super, Madrid, Spain
Quijano-Sanchez, Lara
Liberatore, Federico
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Univ Carlos III Madrid, Santander Big Data Inst UC3M, Madrid, Spain
Cardiff Univ, Sch Comp Sci & Informat, Cardiff, WalesUniv Autonoma Madrid, Escuela Politecn Super, Madrid, Spain
Liberatore, Federico
Rodriguez-Lorenzo, Guillermo
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Univ Autonoma Madrid, Escuela Politecn Super, Madrid, SpainUniv Autonoma Madrid, Escuela Politecn Super, Madrid, Spain
Rodriguez-Lorenzo, Guillermo
Lillo, Rosa E.
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Univ Carlos III Madrid, Santander Big Data Inst UC3M, Madrid, Spain
Univ Carlos III Madrid, Stat Dept, Madrid, SpainUniv Autonoma Madrid, Escuela Politecn Super, Madrid, Spain
Lillo, Rosa E.
Gonzalez-Alvarez, Jose L.
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Minist Interior, Gabinete Coordinac & Estudios, Secretaria Estado Segur, Madrid, SpainUniv Autonoma Madrid, Escuela Politecn Super, Madrid, Spain