Objective To investigate the levels of anxiety, depression and stress and their associated factors, among nursing professionals who make up the team working against COVID-19 of a University Hospital in the south of Brazil. Method Exploratory, descriptive, cross-sectional study conducted from May to July 2020. Results From the total number of professionals, 53.8% had anxiety; 38.4% depression; and 40.3%, stress. Age, length of service in the profession, job satisfaction and work shift showed a statistically significant association with depression, while the employment contract, length of service in the UH, length of service in the unit prior to the opening of the COVID-19 unit and satisfaction at work showed a significant association with stress. Conclusions The nursing professionals of the COVID-19 team have important levels of anxiety, depression and stress, and the factors associated with depression and stress have been identified.
Objetivo: analisar o perfil sociodemográfico e laboral dos profissionais de enfermagem atuantes na unidade de internação COVID-19, que apresentaram, concomitantemente, ansiedade, depressão e estresse. Método: estudo descritivo, transversal realizado de maio a julho de 2020, via formulário eletrônico, contendo um instrumento de caracterização sociodemográfico e laboral, bem como uma escala para avaliar ansiedade, depressão e estresse - Depression Anxiety and Stress Scale-21. Aplicou-se análise descritiva e inferencial. Resultados: A média de ansiedade foi de 19,5; 20,0 para depressão; e 26,2 para estresse. Observou-se alta correlação entre a subescalas depressão e ansiedade (p=0,004). Considerações finais: Os profissionais apresentaram níveis elevados de ansiedade, depressão e estresse. Não foram identificadas variáveis independentes com valores estatisticamente significantes que pudessem esclarecer os fatores associados à ansiedade, depressão e estresse entres os profissionais da enfermagem. Foi possível observar correlação estatisticamente significante entre as subescalas depressão e ansiedade.
Predicting invoice payment is valuable in multiple industries and supports decision-making processes in most financial workflows. However, the challenge in this realm involves dealing with complex data and the lack of data related to decisions-making processes not registered in the accounts receivable system. This work presents a prototype developed as a solution devised during a partnership with a multinational bank to support collectors in predicting invoices payment. The proposed prototype reached up to 77% of accuracy, which improved the prioritization of customers and supported the daily work of collectors. With the presented results, one expects to support researchers dealing with the problem of invoice payment prediction to get insights and examples of how to tackle issues present in real data.
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