Fatores preditores da qualidade de vida de promotores e procuradores de Justiça de um Ministério Público Estadual brasileiro: uma análise de cluster

• Ano de defesa: 2023
• Autor(a) principal: Sandra Filgueiras de Oliveira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; EEFFTO - ESCOLA DE EDUCAÇÃO FISICA, FISIOTERAPIA E TERAPIA OCUPACIONAL; Programa de Pós-Graduação em Estudos da Ocupação; UFMG
• Palavras-chave em Português: Qualidade de vida; Promotores e Procuradores de Justiça; Estresse laboral; Análise de cluster; Saúde do trabalhador; Qualidade de vida no trabalho; Estresse ocupacional
• Link de acesso: http://hdl.handle.net/1843/54315

Public prosecutors (PP) are workers subject to high work demands, restrictions on family life, time dedicated to rest, leisure and social occupations, with harm to physical and mental health. This cross-sectional study investigated the clustering of members of a Brazilian Public Prosecutor’s Office (PPO) based on the similarity of sociodemographic, epidemiological, religious, professional characteristics, socioeconomic development index of the place where they live and work and their quality of life (QoL). The PP (N=355), recruited via email, filled in a sociodemographic questionnaire, the WHOQOL-bref, and the JSS scale. The cluster analysis used the Gower method to form groups of individuals with similar characteristics. Association studies (chi-squared and Fisher's exact) and comparison tests (Kruskal-Wallis) investigated differences in cluster characteristics. A multiple linear regression analysis identified QoL predictors in the groups. Cluster analysis showed 3 groups of workers with similar characteristics but statistically different QoL. The variables explained 48.3% of the total QoL variance in cluster 1 (F(4, 91=21.235; p<0.001; R2=0.483). The final model showed that working years (βsc=0.288; t=3.770; p<0.001), sedentary lifestyle (βsc=-0.274; t=-3.494; p<0.001), sleep quality (βsc=0.402; t=5.212; p<0.001) and perceived social support (βsc=0.296; t=3.772; p<0.001) significantly contributed for the model. Individuals in group 1 had a significantly higher QoL score [68 (61-76)] than those in groups 2 [60 (50-67); p<0.001] and 3 [65 (56-74); p<0.05]. In the second regression model, variables explained 54.4% of the total QoL variance in cluster 2 (F(3, 79)=31.364; p<0.001; R2 =0.544). The final model shows that sleep quality (βsc=0.307; t=3.798; p<0.001), perceived social support (βsc=0.452; t=5.140; p<0.001), and occupational stress (βsc=-0.210; t=-2.481; p<0.05) contributed significantly for the model. Individuals in group 2 had a significantly lower QoL score [60 (50-67)] than those in groups 1 [68 (61-76); p<0.001] and 3 [65 (56-74); p<0.001]. Finally, in cluster 3, variables explained 45.2% of the total QoL variance (F(5, 104)=17.173; p<0.001; R2=0.452). The final model indicated that religiosity, (βsc=0.161; t=2.144; p<0.05), sleep quality (βsc=0.419; t=5.541; p<0.001), perceived social support (βsc=0.214; t=2.743; p<0.05), and occupational stress (βsc=-0.218; t=-2.727; p<0.05) contributed significantly for the model. The data show that the quality of life of these PPO workers is related to personal (life habits), contextual (social support) and occupational (work stress) characteristics. The creation of a predictive model and the use of cluster analysis made it possible to explore predictors of PP's quality of life in order to contribute to the advancement of the theoretical debate on the subject. From a practical point of view, individual strategies to improve the QoL of the investigated population should motivate the adoption of a healthy lifestyle and, above all, organizational intervention strategies should seek to reduce work stress and promote greater social support at work.