Background: Life satisfaction is a determining factor for the improvement of mental and physical health. Health care workers are a vulnerable population to suffer alterations in the factors that affect life satisfaction. Determining the influence of these factors on quality of life is important for their proper management. Objective: To examine sociodemographic factors and healthy behaviors influencing life satisfaction in Peruvian professionals. Methods: A cross-sectional study was conducted with 506 health care workers, who had a mean age of 40.34 years (SD = 10.39). A sociodemographic questionnaire, sleep quality, physical activity, eating habits, and life satisfaction were used. A regression model was fitted with the life satisfaction variable as the dependent variable. Results: In multivariable analysis, age (β = −.938, P < .01) and perception of poor health status (β = −4.743, P < .001) were found to be associated with lower life satisfaction. On the other hand, higher university education level (β = 1.667, P < .001), absence of smoking (β = 3.202, P < .01), absence of depressive symptoms (β = 3.390, P < .001), interest in daily activities (β = 3.503, P < .05), good sleep quality (β = 1.027, P < .01), a high frequency of physical activity (β = 1.056, P < .01), and healthy eating are variables associated with higher life satisfaction. Conclusion: Sociodemographic aspects such as age and the perception of poor health are associated with lower life satisfaction. On the other hand, healthy behaviors such as absence of smoking, absence of depressive symptoms, interest in daily activities, good quality of sleep, high frequency of physical activity, and a healthy diet were associated with higher life satisfaction.

Background: The integration of Artificial Intelligence (AI) into various aspects of daily life has sparked growing interest in understanding public attitudes toward this technology. Despite advancements in tools to assess these perceptions, there remains a need for culturally adapted instruments, particularly in specific contexts like that of Peruvian nurses. Objective: To evaluate the psychometric properties of the AIAS-4 in a sample of Peruvian nurses. Methods: An instrumental design was employed, recruiting 200 Peruvian nurses. The Attitude toward Artificial Intelligence in Spanish (AIAS-S), a cultural and linguistic adaptation of the AIAS-4, involved data analysis using descriptive statistics, confirmatory factor analysis (CFA), and invariance tests. Results: The Confirmatory Factor Analysis (CFA) confirmed a unidimensional factor structure with an excellent model fit (χ² = 0.410, df = 1, p = 0.522, CFI = 1.00, TLI = 1.00, RMSEA = 0.00, SRMR = 0.00). The scale demonstrated high internal consistency (α = 0.94, ω = 0.91). Tests of invariance from configural to strict confirmed that the scale is stable across different demographic subgroups. Conclusions: The AIAS-S proved to be a psychometrically solid tool for assessing attitudes toward AI in the context of Peruvian nurses, providing evidence of validity, reliability, and gender invariance. This study highlights the importance of having culturally adapted instruments to explore attitudes toward emerging technologies in specific groups.

Background: The advancement of artificial intelligence (AI) in education has transformed the way students interact with technological tools, creating new challenges related to self-efficacy, anxiety, and AI dependence. Self-efficacy refers to one's confidence in their ability to use AI, while AI-related anxiety pertains to the fear or concern when interacting with these systems. These variables can influence technological dependence, affecting academic performance and emotional well-being. Objective: This study aims to examine the influence of self-efficacy in AI use and anxiety toward AI on AI dependence among Peruvian university students. Methods: A descriptive cross-sectional study was conducted with 528 Peruvian university students aged 18 to 37 years (M = 19.00, SD = 3.84). Scales were used to measure AI self-efficacy, anxiety toward AI, and AI dependence. Correlation and multiple regression analyses were applied to identify predictors of technological dependence. Results: The results showed that AI self-efficacy was positively correlated with AI anxiety (r = 0.43, p < .01) and AI dependence (r = 0.61, p < .01). Anxiety also significantly correlated with AI dependence (r = 0.71, p < .01). Multiple regression analysis revealed that both AI anxiety (β = 1.131, p < .001) and AI self-efficacy (β = 0.610, p < .001) predicted AI dependence. Additionally, business administration students exhibited greater dependence compared to students from other fields (β = 1.025, p < .05). Conclusions: Students with higher self-efficacy in AI use tend to utilize AI more frequently but also experience greater anxiety and dependence on AI. Educational interventions should focus on reducing AI-related anxiety to prevent excessive dependence, especially among students.

Objective: To determine the association between sociodemographic factors, stress, depression and anxiety, emotional eating, and concern about COVID-19 contagion in first and second-line Peruvian nurses. Methodology: The study was cross-sectional and conducted with 500 Peruvian nurses of both sexes, over 18 years of age. Validated questionnaires were used to measure sociodemographic aspects, concern about COVID-19, generalized anxiety, depression, self-perceived stress, and emotional eating. Multiple linear regression analysis was performed to analyze the factors affecting concern about COVID-19 contagion. Results: The multiple linear regression analysis showed that stress, being between the ages of 18 and 29 years, being male, being from the coastal region or the jungle region, having a bachelor’s degree, severe anxiety, and severe depression were associated with higher concern about COVID-19. On the other hand, having more than 5 to 10 years of experience and more than 10 years of experience, low emotional eating, and non-emotional eating were negatively associated with concern. This model explained 44.05% of the variability among the participating nurses. Conclusion: These findings provide resources for future research on the comprehensive well-being of nursing staff by exploring various sociodemographic aspects and mental conditions associated with greater concern about COVID-19. Meanwhile, years of experience and emotional eating behavior were associated with lower concern about COVID-19. Future studies could incorporate this information to preserve the mental and physical health of nurses in the face of potential occupational threats.

Introduction: Workplace violence is identified as a growing challenge in the healthcare sector, negatively impacting both nursing professionals and the quality of patient care. In this context, emotional intelligence emerges as a key tool for mitigating the effects of workplace violence, facilitating effective stress management, and enhancing interpersonal relationships in the workplace. Method: The study employed a quantitative, correlational, cross-sectional design. The sample consisted of 183 nurses from a National Hospital in Chiclayo, Peru, selected through probabilistic sampling. Two instruments were used: the EVP-health questionnaire to assess workplace violence and the TMMS-24 scale to measure emotional intelligence. Results: Findings indicate that 45,3 % of respondents experienced a high level of workplace violence, while 61,8 % showed a low level of emotional intelligence. Additionally, a negative correlation between workplace violence and emotional intelligence was found, demonstrating that high levels of violence are associated with low levels of emotional intelligence. Conclusions: the study concludes that enhancing emotional intelligence in nurses could be crucial in reducing workplace violence in healthcare settings. The implementation of emotional intelligence training programs for healthcare staff is suggested, not only to improve the work environment but also to enhance the quality of care provided to patients. The research highlights the need to address these aspects to promote a safer and more efficient work environment in the healthcare sector

Background: the crucial role of nurses within the global healthcare system is undeniable, especially considering the high demand and significant stress that characterizes their professional field. From an administrative and human resource management perspective in healthcare, the importance of emotional well-being, along with emotional intelligence and self-efficacy, has emerged as a primary focus of interest in recent research, particularly highlighted in the Peruvian nursing scenario. Objective: this study aimed to understand the relationship between self-efficacy, emotional intelligence, and work engagement among Peruvian nurses, and how these factors might influence the quality of care provided and staff retention in the healthcare sector. Methods: a quantitative analysis was conducted, based on structured surveys that measured levels of self-efficacy, emotional intelligence, and work engagement. The participants were registered nurses in Peru, and the sample was stratified according to different regions and levels of experience. Results: the findings confirmed a positive relationship between self-efficacy and work engagement, corroborating previous research. Furthermore, the positive association between emotional intelligence and work engagement in this professional group was validated. It was evidenced that nurses with higher emotional intelligence and self-efficacy tend to have a greater work engagement, positively affecting the quality of care and their retention in the healthcare sector. Conclusions: emotional intelligence and self-efficacy are fundamental for the work engagement of Peruvian nurses. These factors not only impact the academic realm but also have essential practical significance in the healthcare sector. The promotion and training in these areas could be crucial to ensure quality care and the emotional well-being of nursing staff