The effect of micro-learning on trauma care knowledge and learning satisfaction in nursing students.

BACKGROUND: Despite the fact that there are few formal trauma training courses for nurses, they play an important role in the care of trauma patients. This study aims to investigate the effect of micro-learning on the knowledge of managing trauma patients and learning satisfaction in nursing students. METHODS: The convenience sampling method was used to enroll 30 final-year nursing students from Alborz University of Medical Sciences in this quasi experimental One-group pretest -posttest design. The educational content was created and repeated 4 times over the course of 36 days using a micro-learning approach through whiteboard animations, video casts, and live videos. MCQ scenario-based exam was used to assess participants' knowledge of trauma in three phases: pretest, immediately following the intervention, and one month after the end of the educational program. An e-learning satisfaction psychometric questionnaire was used to measure satisfaction. RESULTS: The mean knowledge score 1 month after the intervention did not differ significantly from the score immediately after the intervention (p = 1), but there was a significant relationship between the mean knowledge score immediately after the intervention and before that (p = 0.047). Demographic variables and knowledge of trauma management did not differ statistically significant. The majority of students were pleased with how the course was implemented (5.64). CONCLUSION: The use of micro-learning has a positive effect on the promotion and retention of knowledge of trauma care, as well as increasing nursing students' satisfaction. Micro-learning is proposed as a new educational approach that can be used as a complementary or as a stand-alone method to convey important educational concepts and increase learner satisfaction.

Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity.

Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.

A sex-dependent computer-aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI.

Objective.Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females. Various studies have suggested the use of brain connectivity features for the diagnosis of ASD. Also, sex-related biological factors have been shown to play a role in ASD etiology and influence the brain connectivity. Therefore, proposing an accurate computer-aided diagnosis system (CADS) for ASD which considers the sex of subjects seems necessary. In this study, we present a sex-dependent connectivity-based CADS for ASD using resting-state functional magnetic resonance imaging. The proposed CADS classifies ASD males from normal males, and ASD females from normal females.Approach.After data preprocessing, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs) followed by applying dual-regression to obtain the time course of each RSN for each subject. Afterwards, functional connectivity measures of full correlation and partial correlation and the effective connectivity measure of bivariate Granger causality were computed between time series of RSNs. To consider the role of sex differences in the classification process, male, female, and mixed groups were taken into account, and feature selection and classification were designed for each sex group separately. At the end, the classification accuracy was computed for each sex group.Main results.In the female group, a classification accuracy of 93.3% was obtained using full correlation while in the male group, a classification accuracy of 86.7% was achieved using both full correlation and bivariate Granger causality. Also, in the mixed group, a classification accuracy of 83.3% was obtained using full correlation.Significance.This supports the importance of considering sex in diagnosing ASD patients from normal controls.