Effectiveness of a Mindfulness-Based Intervention Program to Improve Communication and Stress Coping Skills in University Students.

Investigating the contribution of mindfulness training to psychological well-being and quality of life in the university setting is of interest. The objective of the study is to present a comparative analysis of the scores in the variables of self-efficacy, resilience, coping strategies, and communication skills before and after the application of an intervention program based on mindfulness. An ex post facto cross-sectional design and a convenience sample of participants were adopted. The participants were students belonging to Education Sciences who benefited from the activities of the program. Instruments were administered to assess mindfulness, self-efficacy, resilience, coping strategies, and communication skills. The correlations of the mindfulness variable with the other psychoeducational variables evaluated were also analyzed. The results indicate an increase in the scores in the selected variables of mindfulness, resilience, communication skills, and some of the coping strategies considered productive or functional such as problem solving, self-criticism, emotional expression, desiderative thinking, social support, and cognitive restructuring. Statistically significant correlations were also observed between the variable mindfulness and those of perceived self-efficacy, resilience, coping strategies, and communication skills. The development of mindfulness training programs in the university setting is necessary to contribute to the improvement of more adaptive coping skills and the promotion of resilience.

Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System.

Suicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to analyze the relationship between suicide risk and model variables and (2) to design an artificial neural network (ANN) with predictive capacity for suicide risk. The sample comprised 337 youths aged 18-33 years. An ex post facto design was used. The results showed that emotional attention, followed by problem solving and perfectionism, were variables that contributed the most to the ANN's predictive capacity. The ANN achieved a hit rate of 85.7%, which is much higher than chance, and with only 14.3% of incorrect cases. This study extracted relevant information on suicide risk and the related risk and protective factors via artificial intelligence. These data will be useful for diagnosis as well as for psycho-educational guidance and prevention. This study was one of the first to apply this innovative methodology based on an ANN design to study these variables.