Prediction of the End of a Romantic Relationship in Peruvian Youth and Adults: A Machine Learning Approach.

This study explores the effectiveness of machine learning models in predicting the end of romantic relationships among Peruvian youth and adults, considering various socioeconomic and personal attributes. The study implements logistic regression, gradient boosting, support vector machines, and decision trees on SMOTE-balanced data using a sample of 429 individuals to improve model robustness and accuracy. Using stratified random sampling, the data is split into training (80%) and validation (20%) sets.

The scale of myths of romantic love: Evidence of validity and reliability of a brief scale in Peru.

The study aimed to provide validity evidence and reliability of the Scale of Myths of Romantic Love (SMRL) in Peru among young and adult individuals. Focusing on how romantic love myths affect relationship satisfaction and their ties to interpersonal violence, sexism, and gender inequality, the methodology involved 308 participants, mainly females (75%), using the SMRL and Relationship Assessment Scale. Bayesian Confirmatory Factor Analysis (BCFA) assessed the scale's structure and reliability, complemented by descriptive statistics and correlation analyses to examine the myths' impact on intimate relationships.

Depression, COVID-19 Anxiety, Subjective Well-being, and Academic Performance in University Students With COVID-19-Infected Relatives: A Network Analysis.

This study aimed to examine the relationship between anxiety, depression, subjective well-being, and academic performance in Peruvian university health science students with COVID-19-infected relatives. Eight hundred two university students aged 17-54 years (Mean 21.83; SD = 5.