ABSTRACT
Assessing executive functions in individuals with disorders or clinical conditions can be challenging, as they may lack the abilities needed for conventional test formats. The use of more personalized test versions, such as adaptive assessments, might be helpful in evaluating individuals with specific needs. This paper introduces PsycAssist, a web-based artificial intelligence system designed for neuropsychological adaptive assessment and training. PsycAssist is a highly flexible and scalable system based on procedural knowledge space theory and may be used potentially with many types of tests. We present the architecture and adaptive assessment engine of PsycAssist and the two currently available tests: Adap-ToL, an adaptive version of the Tower of London-like test to assess planning skills, and MatriKS, a Raven-like test to evaluate fluid intelligence. Finally, we describe the results of an investigation of the usability of Adap-ToL and MatriKS: the evaluators perceived these tools as appropriate and well-suited for their intended purposes, and the test-takers perceived the assessment as a positive experience. To sum up, PsycAssist represents an innovative and promising tool to tailor evaluation and training to the specific characteristics of the individual, useful for clinical practice.
ABSTRACT
DOI: https://doi.org/10.7334/psicothema2023.193 Text: This article was originally published with errors, which have now been corrected in the online version: 1. The investigated alternative models have now been described more clearly. The method for comparing them with the original model has been correctly specified based only on Akaike's Information Criterion (AIC) and not on the delta Comparative Fit Index, given that the models are not nested. 2. The formula for computing the critical value to which comparing the Mardia's index for verifying if the data are multivariate normally distributed is equal to k(k+2) and not, as previously written, equal to k(k+1). 3. We have now specified that, in the Structural Equation Model, the correlations introduced between the dependent variables are correlations between the unexplained variance and thus may be described as partial correlations. 4. Finally, we have corrected the direction of the arrows of the lines from Participating, Consuming, and Expert Using observed variables toward the corresponding Cultural Capital latent variable and from Bonding and Bridging observed variables toward the corresponding social capital latent variable. DOI of original article: (https://doi.org/10.7334/ psicothema2021.231)
ABSTRACT
BACKGROUND: Sociocultural level (SCL) comprises Socioeconomic Status (SES), Cultural Capital (CC), and Social Capital (SC). The relationships between all SCL dimensions have never been investigated. This study aimed to develop a structural equation model representing how age affects the relationships between educational level, occupational prestige (as a measure of SES), CC, and SC for men and women. METHOD: SES, dimensions of CC and SC were measured with valid scales for 654 adults (63% female) aged 19 to 74 years ( M[SD] = 42.86 [13.32]), that had or used to have an occupation and the majority of whom had at least a university degree (65%). All lived in a medium-sized town in Italy. RESULTS: Age affected the interrelated indicators of SES (educational level and occupational prestige), which in turn affected the interrelated dimensions CC and SC (CFI = .97; RMSEA = .073 [CI = .053 - .095]; SRMR = 0.031). The system of relationships was simpler in men than in women, with educational level being less relevant in affecting the other constructs. CONCLUSIONS: The hierarchical structure of SCL and effect of age and gender must be properly taken into account in studies on the effects of SCL on human behavior.
Subject(s)
Social Capital , Adult , Educational Status , Female , Humans , Italy , Male , Social ClassABSTRACT
Background: Sociocultural level (SCL) comprises Socioeconomic Status(SES), Cultural Capital (CC), and Social Capital (SC). The relationshipsbetween all SCL dimensions have never been investigated. This study aimedto develop a structural equation model representing how age affects therelationships between educational level, occupational prestige (as a measureof SES), CC, and SC for men and women. Method: SES, dimensions ofCC and SC were measured with valid scales for 654 adults (63% female)aged 19 to 74 years (M[SD] = 42.86 [13.32]), that had or used to havean occupation and the majority of whom had at least a university degree(65%). All lived in a medium-sized town in Italy. Results: Age affectedthe interrelated indicators of SES (educational level and occupationalprestige), which in turn affected the interrelated dimensions CC and SC(CFI = .97; RMSEA = .073 [CI = .053 - .095]; SRMR = 0.031). The systemof relationships was simpler in men than in women, with educational levelbeing less relevant in affecting the other constructs. Conclusions: Thehierarchical structure of SCL and effect of age and gender must be properlytaken into account in studies on the effects of SCL on human behavior.
Antecedentes: el Nivel Sociocultural(NSC) es compuesto de Estatus Socioeconómico (ESS), Capital Cultural(CC) y Capital Social (CS). Nunca se han investigado las relaciones entrelas dimensiones del NSC. Este estudio tiene como objetivo desarrollar unmodelo de ecuaciones estructurales que represente cómo afecta la edada las relaciones entre el nivel educativo, el prestigio ocupacional (comomedida del ESS), el CC y el CS en hombres y mujeres. Método: el niveleducativo, el prestigio ocupacional, las dimensiones del CC y el CS semidieron con escalas validadas en 654 adultos (63% mujeres), de 19 a 74años de edad, la mayoría en posesión de al menos un título universitario(65%), que tenían o habían tenido una ocupación laboral. Todos vivían enel municipio de una ciudad italiana de tamaño medio. Resultados: la edadafecta a los indicadores interrelacionados del ESS, que a su vez afectan alas dimensiones interrelacionadas de CC y CS (CFI = .97; RMSEA = .073[CI = .053 - .095]; SRMR = 0.031). Conclusiones: la estructura jerárquicadel NSC y los efectos sobre el mismo de la edad y el género deben sertenidos en cuenta en el estudio de los efectos del NSC
