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 academic and emotional challenges faced by medical students can affect critical thinking and may also contribute to the development of increased generalized anxiety. Similarly, critical thinking and generalized anxiety can impact study satisfaction through the mediating mechanism of academic self-efficacy. Objective: The aim of this study was to assess the mediating role of academic self-efficacy between critical thinking and generalized anxiety in study satisfaction among medical students. Methods: A cross-sectional and explanatory study was conducted involving 259 Peruvian medical students aged between 18 and 35 (M = 20.29, SD = 2.84). The evaluation was based on self-reported questionnaires covering critical thinking, generalized anxiety, academic self-efficacy, and study satisfaction. Furthermore, a structural equation modeling (SEM) and mediation approach was employed to examine the relationships between variables. Results: The results showed an adequate fit of the model [χ² (87) = 155, p < 0.001, CFI = 0.93, TLI = 0.92, RMSEA = 0.05 (CI: 0.04–0.07), SRMR = 0.07], demonstrating the impact of critical thinking and generalized anxiety. It was confirmed that academic self-efficacy has a positive effect on study satisfaction. Moreover, the mediating role of academic self-efficacy was confirmed between critical thinking and study satisfaction, as well as between generalized anxiety and study satisfaction. Conclusions: Due to the high academic load on medical students, academic self-efficacy plays a mediating role in the relationship between critical thinking, generalized anxiety, and satisfaction with studies. The development of educational strategies will help to promote critical thinking and academic self-efficacy, as well as provide support to students with generalized anxiety, to enhance study satisfaction.

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.

AI has revolutionized the workplace, significantly impacting the nursing profession. Attitudes toward AI, defined as workers’ perceptions and beliefs about its utility and effectiveness, are critical for its adoption and efficient use in clinical settings. Factors such as age, marital status, and education level may influence this relationship, affecting job performance. This study examines the influence of attitude toward AI on job performance with AI among Peruvian nurses, while also assessing how sociodemographic characteristics moderate this relationship. A descriptive cross-sectional design was used with a sample of 249 Peruvian nurses aged 24 to 53 years (M = 35.58, SD = 8.3). Data were collected using two validated scales: the Brief Artificial Intelligence Job Performance Scale (BAIJPS) and the Attitude toward Artificial Intelligence Scale (AIAS-4). Descriptive statistics, Pearson correlations, and multiple linear regression were applied. A significant positive correlation was found between attitude toward AI and job performance with AI (r = 0.43, p < 0.01). Age (β = -0.177, p < 0.05), divorced marital status (β = -8.144, p < 0.01), and having a bachelor’s degree (β = -3.016, p < 0.05) were negatively associated with job performance, while being from the Selva region had a positive effect (β = 4.182, p < 0.05). A favorable attitude toward AI positively influences nurses’ job performance, highlighting the need for interventions that enhance AI perception. Age, marital status, and education moderate this relationship, suggesting AI adoption strategies should be tailored to different demographic groups.

Background: Performance evaluation is essential to ensure quality healthcare services, especially in the field of nursing. Objective: The objective of this study was to analyze the factorial structure, reliability, and invariance by sex and age of the work performance scale in Peruvian nurses. Methods: Confirmatory factor analysis (CFA) was conducted to evaluate the internal structure of the scale, and psychometric properties including reliability and convergent validity were determined. Additionally, factorial invariance was evaluated according to participants' sex and age. Results: The CFA supported the structure of three factors (Task Performance, Counterproductive Behaviors, Contextual Performance) and showed adequate and stable psychometric properties for a 12-item version (: χ2 = 231.09, df = 78; CFI = 0.97, TLI = 0.96, RMSEA = 0.06 (90% CI: 0.05-0.06), and SRMR = 0.03). Strict factorial invariance was demonstrated for both sex and age, and adequate internal consistency was found for each dimension, as well as convergent validity. Conclusions: The work performance scale, in its 12-item version (IWPQ-P), is a valid and reliable measure for evaluating work performance in Peruvian nurses. Its factorial invariance by sex and age makes it a useful tool for future research and practical applications in nursing performance evaluation. Antecedentes: La evaluación del desempeño es esencial para asegurar servicios de atención sanitaria de calidad, especialmente en el campo de enfermería. Objetivo: El objetivo de este estudio fue analizar la estructura factorial, la fiabilidad y la invarianza por sexo y edad de la escala de desempeño laboral en enfermeros peruanos. Métodos: Se realizó un análisis factorial confirmatorio (AFC) para evaluar la estructura interna de la escala, y se determinaron propiedades psicométricas incluyendo la fiabilidad y la validez convergente. Adicionalmente, se evaluó la invarianza factorial según el sexo y la edad de los participantes. Resultados: El AFC respaldó la estructura de tres factores (Desempeño de Tareas, Comportamientos Contraproducentes, Desempeño Contextual) y mostró propiedades psicométricas adecuadas y estables para una versión de 12 ítems (χ² = 231.09, df = 78; CFI = 0.97, TLI = 0.96, RMSEA = 0.06 (IC del 90%: 0.05-0.07), y SRMR = 0.03). Se demostró invarianza factorial estricta tanto para el sexo como para la edad, y se encontró una consistencia interna adecuada para cada dimensión, así como validez convergente. Conclusiones: La escala de desempeño laboral, en su versión de 12 ítems (IWPQ-P), es una medida válida y fiable para evaluar el desempeño laboral en enfermeros peruanos. Su invarianza factorial por sexo y edad la convierte en una herramienta útil para futuras investigaciones y aplicaciones prácticas en la evaluación del desempeño de enfermería.

Background: Medical education represents a complex field of study, influenced by various psychological, demographic, and contextual factors. Academic motivation, essential for educational success, has been linked to critical decisions in medical careers and can be modulated by contextual elements such as socioeconomic and geographical environments. The theory of self-determination has provided a solid framework for understanding the multidimensional nature of motivation. Objective: To evaluate the psychometric properties of a Spanish version of the Short Scale of Academic Motivation among Peruvian medical students. Methods: Using an instrumental design, the factorial structure, reliability, and gender invariance of the SAMS-S were assessed. A confirmatory factor analysis (CFA) was conducted to validate the scale’s structure based on seven dimensions. Additionally, reliability was assessed through Cronbach’s alpha coefficient and omega coefficient, and gender invariance was determined through multi-group confirmatory factor analysis. Results: The Peruvian version of the SAMS-S showed a good fit in the CFA with satisfactory goodness-of-fit indices. However, challenges in discriminant validity among certain dimensions were detected, suggesting the presence of a second-order factor. The proposed second-order model yielded an adequate fit (χ² = 198.26, df = 70, CFI = 0.92, TLI = 0.90, RMSEA = 0.08 [90% CI: 0.07–0.1], SRMR = 0.07), validating the factorial structure of the SAMS-S. The scale’s reliability and its subscales were within acceptable ranges. Furthermore, the gender invariance of the SAMS-S was confirmed at all levels, from configural to strict. Conclusions: The second-order model of the SAMS-S presents as a valid and reliable tool for measuring academic motivation among medical students in Peru. Its robustness and adaptability make it relevant for future research in similar educational contexts and can serve as a basis for interventions aimed at improving academic motivation in this specific group.