El objetivo de esta investigación es analizar la relación entre la inteligencia emocional (IE), el conflicto trabajo familia (CTF), la satisfacción laboral (SL), el desempeño (D) y la intención de dejar el trabajo (IDT), en el contexto de los docentes de educación primaria semi-particular de la región de Antofagasta (Chile). Fueron seleccionados 140 docentes (81% mujeres) a través de un muestreo no probabilístico por conveniencia. Este es un estudio de enfoque cuantitativo con medidas validadas en forma de cuestionarios de auto-respuesta. El análisis de los datos se realizó mediante el método de dos etapas en ecuaciones estructurales. Los resultados señalan que el modelo planteado tiene una significativa e importante capacidad de explicar el desempeño, la satisfacción laboral y la intención de dejar el trabajo de los docentes. Así mismo, es posible concluir que la inteligencia emocional juega un importante rol en la satisfacción laboral y el desempeño de los docentes.

In recent years, network psychometrics has emerged as an alternative to the reflective latent variable model. This model conceptualizes traits as complex systems of behaviors mutually interacting with each other. Although this model offers important advantages compared to the reflective model, questions remain regarding the necessary sample size and the influence of factors such as the number of nodes and edges. This study aims to evaluate the psychometric network model performance under different conditions of sample size, number of nodes, and number of edges. The methodology involved a simulation with 1000 replicates for each combination of sample size, number of nodes, and the value of gamma parameter, which is used to determine the magnitude of the edges considered significant. The effect of these conditions on the accuracy of edge estimations and centrality indices (strength and expected influence) was assessed using sensitivity, specificity, and bias indicators. Results suggest that sample size and network complexity have a more significant impact than γ, methodological guidelines being proposed to support decision-making in applied research. In summary, this study provides empirically grounded recommendations that can guide applied researchers in designing robust psychometric network analyses and ensuring reliable estimation of model parameters.

Previous research has shown the benefits of early detection and treatment of dementia. This detection is usually performed manually by one or more clinicians based on reports and psychometric testing. Machine learning algorithms provide an alternative method of prediction that may contribute, with an automated process and insights, to the diagnosis and classification of the severity level of dementia. The aim of this study is to explore the use of neuropsychological data from a reduced version of the Addenbrooke’s Cognitive Examination III (ACE-III) to predict absence or different levels of dementia severity using the Global Deterioration Scale (GDS) scores through the implementation of the kNN machine learning algorithm. A sample of 1164 elderly people over sixty years old were evaluated using a reduced version of the ACE-III and the GDS. The kNN classifier provided good accuracies using 15 items from the ACE-III and adequately differentiating people with absence and mild impairment, from those with more severe levels of impairment according to the GDS rating. Our results suggest that the kNN algorithm may be used to automate aspects of clinical cognitive impairment classification in the elderly population.

Background: The Picture Free and Cued Selective Reminding Test (P-FCSRT) has proven useful in assessing episodic memory while controlling for interference from attentional processes. In Chile, studies have supported its diagnostic utility in detecting dementia in older adults, however, it lacks both normative data that account for the influence of age and educational level from mid-adulthood onwards, and the quantification of intrusive errors which have significant clinical value. Objective: The aim of this study is to determine normative scores and intrusive errors for the P-FCSRT, controlling for sociodemographic factors (age and educational level) in the Chilean population. Methods: A total sample of 185 healthy participants from the Aging Mets cohort were recruited from three regions in Chile (Antofagasta, Santiago, and Puerto Montt). The P-FCSRT was administered to all participants. A multivariate regression-based normative approach was used to generate normative data, considering the effects of age, years of schooling, and sex. Results: Significant effects of age and years of schooling were found on P-FCSRT free recall, total recall, delayed recall, intrusions, and sensitivity to cues. Age had a distinct effect on each P-FCSRT dimension, whereas years of schooling had a consistent effect across them. Sex did not influence any P-FCSRT dimension. Conclusions: This study is the first to provide normative data for the Chilean version of the P-FCSRT and will be beneficial for clinical neuropsychologists in improving the procedures for a more accurate assessment of episodic memory and related impairments.