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Religiosity and Healthy Behaviors in Seventh-day Adventist Church Members in Peru: A Cross-sectional Study por Saintila, Jacksaint, Santana, Laura E. Baquedano, Huancahuire Vega, Salomon, Gálvez-Díaz, Norma Del Carmen, Calizaya Milla, Yaquelin Eveling

“…Methods: A cross-sectional online survey was conducted between June and November 2022. …”
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The mediating role of healthy behaviors and self-perceived health in the relationship between eating behaviors and comorbidity in adults por Ramos‐Vera, Cristian, Quispe-Callo, Gleni, Basauri-Delgado, Miguel, Calizaya Milla, Yaquelin Eveling, Casas-Gálvez, Christian, Gálvez-Díaz, Norma Del Carmen, Saintila, Jacksaint

“…METHODS: A cross-sectional predictive study using data from 5,247 adults from the Health Information National Trends Survey (HINTS) 5, cycle 3 (2019) was conducted. Structural equation modeling (SEM) was used to assess the mediating effect. …”
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Determinants of financial inclusion in households in Peru por Quispe Mamani, Julio Cesar, Pinto, Santotomas Licimaco Aguilar, Calcina-Álvarez, Dominga Asunción, Quispe-Layme, Marleny, Gutierrez Toledo, Guino Percy, Condori Condori, Gina Tamara, Vargas-Espinoza, Luis, Quispe Layme, Wilian, Marca-Maquera, Hugo Rubén, Rosado-Chavez, Charles Arturo

“…A quantitative approach was considered, which was non-experimental with a descriptive and correlational design and in which 81,441 pieces of data were obtained from the National Household Survey (ENAHO) of the National Institute of Statistics and Informatics, applying a logit binomial regression. …”
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Cross-sectional study of the association between pain and depression in patients with brachial plexopathy por Tito-Huaraqui, Erik Michael, Silveira-Machado, Larissa, Huancahuire Vega, Salomon, Soriano‐Moreno, David R.

“…Analytical cross-sectional study based on an online survey of patients with a diagnosis of brachial plexopathy in Latin America. …”
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Concern about COVID-19 infection and workload as predictors of psychological distress during the health emergency in Peruvian healthcare personnel por Carranza Esteban, Renzo Felipe, Mamani Benito, Oscar Javier, Quinteros Zuñiga, Damaris Susana, Farfan Solis, Rosa Luz

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Cross‐Cultural and Psychometric Validation of the Ageism in the Organizational Context Scale in Nursing Students por Silva Fhon, Jack Roberto, Leitón‐Espinoza, Zoila Esperanza, Arpasi Quispe, Orfelina, Villanueva‐Benites, Maritza Evangelina, Bello Vidal, Catalina, Capa‐Luque, Walter

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The Mental Health of the Peruvian Older Adult during the COVID-19 Pandemic por Silva Fhon, Jack Roberto, Villanueva‐Benites, Maritza Evangelina, Goméz-Luján, Maria del Pilar, Mocarro-Aguilar, Maria Rosario, Arpasi Quispe, Orfelina, Peralta-Gómez, Reyna Ysmelia, Lavado-Huarcaya, Sofia Sabina, Leitón‐Espinoza, Zoila Esperanza

“…The data was collected using a web-based survey, and the instruments of demographic data; exposure to information (radio, television, and internet); and presence of physical symptoms, anxiety, and perceived stress were used. …”
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Machine learning models for predicting mortgage payment difficulties in Peru por Geraldo Campos, Luis Alberto, Carreño-Flores, Oscar David, Soria Quijaite, Juan Jesus

“…The objective of this study was to analyze which machine learning models best predict mortgage payment difficulties in Peru.A quantitative method was used with longitudinal data from 2018 to 2022 from the National Household Survey (ENAHO), where a total of 5,716 households with mortgage loans were examined.The input variables considered were geographical area, type of housing, use of credit, and source of financing, with difficulty in meeting the payment schedule as the output variable.The analyses were performed in Google Colab, reporting frequency statistics and exploratory and class balancing analyses to evaluate machine learning models such as Logistic Regression, Random Forest (RF), and Support Vector Machine (SVM) with SMOTE.In the training phase of the classification models, the Scikit-learn, XGBoost, and Keras models were trained and compared.The results showed that, of all the models evaluated, Random Forest without adjustments showed the best performance (F1-score = 0.67; recall = 0.71), although combined Stacking (RF + XGBoost) showed a better balance between classes, but its overall performance was lower.In addition, models such as SVM without adjustments show problems in situations of unbalanced classes, highlighting the need to use techniques such as SMOTE.It is concluded that the Random Forest model is more effective in detecting payment difficulties in mortgage loans.…”
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Análisis y proyección del precio de energía en américa latina por Huaringa Tello, Luis Enrique, Balbin De La Cruz, Victor

“…Los datos del consumo de electricidad en el sector industrial brasileño se pueden organizar en una estructura jerárquica compuesta por cada región geográfica (Sur, Sureste, Centro-Oeste, Noreste y Norte) y sus respectivos estados. …”
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