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Item type:Publication, Factores psicológicos asociados al juego problemático en estudiantes universitarios de Lima(Pontificia Universidad Católica del Perú, 2022-12-15)The aim of the study was to analyze the psychological variables that predict problem gambling in 173 engineering students, men and women, from the first years of study at a private university in Lima, Perú. The age range was 16 to 23 years (M = 17.9, SD = 1.2).The measures were the Personality Inventory NEO FFI (NEO Five-Factor Inventory, Costa & McCrae, 1992), the Academic Stress Inventory (SISCO, Barraza, 2007a), the Time Management Behavior Questionnaire (TMBQ, Macan, 1994) version translated into Spanish by García-Ros and Pérez-González (2012), and the South Oaks Gambling Screen, Revised for Adolescents (SOGS-RA, Becoña, 1997). The results indicated that the management of free time is a moderate predictor of problem gambling (standardized coefficient = -.33), followed by the agreeableness personality´s factor (standardized coefficient = -.29), while academic stress showed lower predictive capacity (standardized coefficient = .10). The results are discussed in relation to possible ways to prevent problem gambling. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estrés académico en universitarios peruanos: importancia de las conductas de salud, características sociodemográficas y académicas(Universidad de San Martín de Porres, 2021-12-23)Antecedentes: la etapa universitaria enfrenta a los estudiantes a situaciones específicas del contexto que podrían resultarles estresantes. Para reconocer esta experiencia se propone el término estrés académico, el cual desencadena síntomas que pueden afectar su salud. describir la prevalencia del estrés académico, sus componentes y analizar el rol de variables sociodemográficas, académicas y conductuales en las dimensiones del estrés. Método: se contó con 1801 universitarios de 6 ciudades del Perú, de los cuales 57.7% fueron mujeres y sus edades estuvieron entre los 18 y 54 años (M = 20.79, DE = 2.69). A ellos, se les aplicó el Inventario SISCO y el Cevju-Perú. Resultados: el 83% de estudiantes refiere haber experimentado estrés académico durante el semestre, con mayor presencia de niveles medio y medio alto. En los análisis de regresión lineal múltiple para cada indicador de estrés, se hallaron modelos medianos para Intensidad del estrés (R2 = .16, p < .001) y Frecuencia de estresores (R2 = .13, p < .001); así como grandes para Síntomas (R2 = .32, p < .001). Los hábitos de salud tuvieron mayor efecto en todos los modelos. Conclusiones: se encuentra que el estrés académico es una problemática relevante en la población estudiada; se evidencia el rol predictivo del sexo, la motivación para el estudio y la mayoría de conductas de salud en los indicadores de estrés académico. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Health and University Students: The Mediator Role of Emotion Regulation Between Academic Stressors and Health Behaviors(SAGE Publications Inc., 2025-03-01)To determine the role of Health Behaviors and Cognitive Emotion Regulation Strategies in the relationship between Health and Academic Stress. Design A cross-sectional study with in-person administration of questionnaires. Setting Data collection t2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Characterization of the stress level of university students using data mining algorithms(Frontiers Media, 2025-11-21)There is concern about the levels of stress faced by college students and their effects on mental health and academic performance. This study aimed to characterize academic stress levels in college students, using data mining algorithms to classify and predict risk patterns. Data were collected from 287 students using the SISCO Academic Stress Inventory, and classification algorithms and association rules were applied using WEKA software. The results revealed that 75.3% of the students experienced high stress levels, primarily linked to psychological reactions and academic demands. It also compared the predictive performance of 13 algorithms, where J48, LMT, and SimpleLogistic achieved classification accuracies above 89%, surpassing results previously reported in similar educational contexts. Association rule mining further showed that being single and childless was strongly correlated with elevated stress levels, highlighting demographic risk profiles often overlooked in earlier research. By integrating predictive modeling with demographic and behavioral factors, this study extended prior literature by showing how data mining can simultaneously classify and explain academic stress, offering actionable insights for universities to design targeted, evidence-based interventions.2