Mental health is considered an indicator of well-being in which people perceive their own potential, can face adverse life situations, function adequately and fruitfully, and are capable of contributing to society. In this sense, the objective of the present investigation was to evaluate depression, anxiety, and stress in Peruvian university students upon their return to face-to-face classes. The quantitative approach was used, the design was non-experimental, and the type was descriptive of a cross-section. The sample consisted of 746 students who were administered the Depression Anxiety Stress Scale, an instrument with adequate psychometric properties. The results indicate that the students presented low levels of depression and anxiety. However, they presented moderate levels of stress. High, direct, and significant correlations were also reported between depression and anxiety (rs = 0.850; p < 0.05), between depression and stress (rs = 0.840; p < 0.05), and between anxiety and stress (rs = 0.836; p < 0.05). It was also determined that depression, anxiety, and stress were significantly associated with some sociodemographic factors such as gender and age group (p < 0.05). It was concluded that the students presented symptoms of depression, anxiety, and stress. Therefore, it is necessary for university authorities to implement strategies for the evaluation and prevention of these symptoms and for the promotion of better mental health in future professionals to promote their personal well-being and quality of life.

Teacher job satisfaction is an important aspect of academic performance, student retention, and teacher retention. We propose to determine the predictive model of job satisfaction of basic education teachers using machine learning techniques. The original data set consisted of 15,087 instances and 942 attributes from the national survey of teachers from public and private educational institutions of regular basic education (ENDO-2018) carried out by the Ministry of Education of Peru. We used the ANOVA F-test filter and the Chi-Square filter as feature selection techniques. In the modeling phase, the logistic regression algorithms, Gradient Boosting, Random Forest, XGBoost and Decision Trees-CART were used. Among the algorithms evaluated, XGBoost and Random Forest stand out, obtaining similar results in 4 of the 8 metrics evaluated, these are: balanced accuracy of 74%, sensitivity of 74%, F1-Score of 0.48 and negative predictive value of 0.94. However, in terms of the area under the ROC curve, XGBoost scores 0.83, while Random Forest scores 0.82. These algorithms also obtain the highest true-positive values (479 instances) and lowest false-negative values (168 instances) in the confusion matrix. Economic income, satisfaction with life, self-esteem, teaching activity, relationship with the director, perception of living conditions, family relationships; health problems related to depression and satisfaction with the relationship with colleagues turned out to be the most important predictors of job satisfaction in basic education teachers.

Teacher life satisfaction is crucial for their well-being and the educational success of their students, both essential elements for sustainable development. This study identifies the most relevant predictors of life satisfaction among Peruvian teachers using machine learning. We analyzed data from the National Survey of Teachers of Public Basic Education Institutions (ENDO-2020) conducted by the Ministry of Education of Peru, using filtering methods (mutual information, analysis of variance, chi-square, and Spearman’s correlation coefficient) along with embedded methods (Classification and Regression Trees—CART; Random Forest; Gradient Boosting; XGBoost; LightGBM; and CatBoost). Subsequently, we generated machine learning models with Random Forest; XGBoost; Gradient Boosting; Decision Trees—CART; CatBoost; LightGBM; Support Vector Machine; and Multilayer Perceptron. The results reveal that the main predictors of life satisfaction are satisfaction with health, employment in an educational institution, the living conditions that can be provided for their family, and conditions for performing their teaching duties, as well as age, the degree of confidence in the Ministry of Education and the Local Management Unit (UGEL), participation in continuous training programs, reflection on the outcomes of their teaching practice, work–life balance, and the number of hours dedicated to lesson preparation and administrative tasks. Among the algorithms used, LightGBM and Random Forest achieved the best results in terms of accuracy (0.68), precision (0.55), F1-Score (0.55), Cohen’s kappa (0.42), and Jaccard Score (0.41) for LightGBM, and accuracy (0.67), precision (0.54), F1-Score (0.55), Cohen’s kappa (0.41), and Jaccard Score (0.41). These results have important implications for educational management and public policy implementation. By identifying dissatisfied teachers, strategies can be developed to improve their well-being and, consequently, the quality of education, contributing to the sustainability of the educational system. Algorithms such as LightGBM and Random Forest can be valuable tools for educational management, enabling the identification of areas for improvement and optimizing decision-making.

The objective of the research was to identify the determinants of poverty at the household level in Peru in 2020. The research design was descriptive and correlational, with a type of non-experimental research and quantitative approach, and considered the logit econometric model; the sources of information used correspond to the National Household Survey of the National Institute of Statistics and Informatics, from which the variables that are considered determinants of poverty were extracted and managed. It was determined that the size of the household positively influences by 1.3%; the economic income of the head of household negatively influences by 0.000828%; the years of education of the head of household influences by 0.1%; homeownership influences by 0.9%; access to social programs of food and non-food aid influence by 0.9% and 0.6%, respectively; access to drinking water service, hygienic service and electric power service have a negative influence of 1.8%, 0.6% and 1.7%; all these factors are associated with the poverty of households in Peru. Therefore, the social determinants of poverty were household size, economic income, years of education, access to homeownership, access to a social food aid program, access to a social non-food aid program, access to drinking water services, access to hygienic services and access to electric power services.

La actividad física es fundamental para el bienestar y la salud general, pero suele ser descuidada por la población universitaria debido a las exigencias académicas. Durante los años universitarios, muchos estudiantes experimentan una disminución en sus niveles de actividad física, influenciados por horarios de estudio intensos, cambios en el estilo de vida y una mayor independencia. Este cambio a menudo conlleva un incremento en los hábitos sedentarios, que pueden tener consecuencias negativas en su salud física y mental. Esta investigación tuvo como objetivo describir los niveles de actividad física en los estudiantes universitarios de la Amazonía peruana. Se llevó a cabo un estudio cuantitativo descriptivo de comparación entre grupos, con un diseño transversal y una muestra de 280 estudiantes universitarios. Para la recolección de datos se utilizó el Cuestionario Internacional de Actividad Física (IPAQ), un instrumento con adecuadas propiedades métricas. Los resultados indican que el nivel de actividad física reportado por el 60.5% de estudiantes era moderado, del 32.1% era alto, mientras que del 7.4% era bajo. Además, se determinó que existe una asociación estadísticamente significativa entre el nivel de actividad física y el género, la edad y la carrera profesional. Se concluyó que el nivel de actividad física de la mayoría de los estudiantes universitarios de la Amazonía peruana era moderado. Por lo tanto, se recomienda que las universidades promuevan la práctica de actividades físicas, integrando programas y políticas que fomenten un estilo de vida saludable entre los estudiantes. Palabras clave: actividad física, sedentarismo, educación superior, estilo de vida, estudiantes universitarios. Physical activity is fundamental for overall well-being and health, but it is often neglected by university students due to academic demands. During university years, many students experience a decrease in their levels of physical activity, influenced by intense study schedules, lifestyle changes, and increased independence. This shift often leads to an increase in sedentary habits, which can have negative consequences for their physical and mental health. This research aimed to describe the levels of physical activity among university students in the Peruvian Amazon. A descriptive quantitative study with a cross-sectional design and group comparison was conducted, using a sample of 280 university students. Data collection utilized the International Physical Activity Questionnaire (IPAQ), an instrument with adequate metric properties. The results indicated that 60.5% of students reported moderate levels of physical activity, 32.1% reported high levels, and 7.4% reported low levels. Additionally, a statistically significant association was found between physical activity levels and gender, age, and major. It was concluded that most university students in the Peruvian Amazon engaged in moderate levels of physical activity. Therefore, it is recommended that universities promote physical activity by integrating programs and policies that encourage a healthy lifestyle among students. Keywords: physical activity, sedentarism, higher education, lifestyle, and university students.

The objective of the research was to determine the effect of public services in the household on poverty in Peru, in the period 2021, for which a quantitative, non-experimental research approach was considered with a descriptive and correlational design. The information from the National Household Survey of the National Institute of Statistics and Informatics (INEI) database was used, considering the modules “Dwelling and Household Characteristics”, “Household Members’ Characteristics”, “Education”, “Employment and Income”, “Household Equipment”, “Summaries (Calculated Variables)” and “Citizen Participation”. It was possible to determine that the following variables had negative effects on household poverty in Peru: access to potable water, sanitation, electric power, cell phone services; achieving higher, secondary, and primary education levels; having a washing machine, motorcycle, tricycle, motorcycle taxi, computer, kitchen, refrigerator in the household; having a property title; being part of an association or organization; living in a rural residence area; and having remittances. However, the number of household members had a positive effect on poverty. Therefore, it was concluded that access to public services in the household contributed to reducing the probability of being poor in Peru.