Principal components to evaluate the minimum intervention in cariology in a Cuban curriculum

• Autor(a) principal: Gil, Alain Manuel Chaple
• Data de Publicação: 2021
• Outros Autores: Abreu, Estela de los Ángeles Gispert
• Tipo de documento: preprint
• Idioma: spa
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/2589
• topic: factor analysis; main components; teaching; learning; dental caries; minimal intervention; análisis factorial; componentes principales; enseñanza; aprendizaje; caries dental; mínima intervención

Introduction: The evaluations of the knowledge system conceived in university careers require practical tools to specify the search for weaknesses in the different processes. Objective: to determine the main components of an instrument to evaluate the cognitive-practical capacities necessary for the teaching-learning of the minimum intervention in the treatment of dental caries in the Stomatology program of studies. Methods: descriptive cross-sectional study. 52 teachers participated in the retest previously carried out in a reliability study. The data from the pilot study resulting from the validation process of the form were used to perform the parametric analyzes that will be described. From this, the values ​​of the evaluation carried out in the retest were extracted, adjusting it to an acceptable sample for this type of study. The Kaiser-Meyer-Olkin test and Bartlett's sphericity test, analysis of communalities, analysis of variance and the regrouping of the items in the proposed dimensions was carried out with the Varimax rotation. Results: the Kaiser-Meyer-Olkin exceeded the acceptable minimum (0.763) and the level of significance was p <0.000 according to the Bartlett test, so the factor analysis was appropriate. It was observed that all the variables were well explained by the model with figures between 85.8% and 97.7% of variability. The first 6 components have greater weight to explain the data, with respective figures of 29.6%, 24.0%, 14.0%, 10.9%, 8.4% and 6.7%, therefore they explain 93.6% of the variability. Conclusions: The main components were renamed according to the factors they grouped.