Artificial intelligence in the analysis of emotions of nursing students undergoing clinical simulation.

Objective: to assess nursing students' emotions undergoing maternal-child clinical simulation.

Methods: an observational study, carried out between June and July 2019. The Focus Group technique was used, with 28 nursing students, randomly distributed into three groups, with qualitative (Bardin technique) and quantitative data (Artificial Intelligence) analysis, to analyze emotions through facial expressions, tone of voice and description of speeches.

Benefits of Qigong as an integrative and complementary practice for health: a systematic review.

Objective: to analyze, in the literature, evidence about the benefits of the integrative and complementary practice of Qigong with regard to the health of adults and the elderly.

Method: a systematic review by searching for studies in the PubMed, CINAHL, LILACS, EMBASE and Cochrane Library databases. Randomized and non-randomized clinical trials were included; in Portuguese, English and Spanish; from 2008 to 2018.

Emotional recognition for simulated clinical environment using unpleasant odors: quasi-experimental study.

Objective: to compare the effect of exposure to unpleasant odors in a simulated clinical environment on the emotions of undergraduate nursing students.

Method: quasi-experimental study. A total of 24 nursing students participated the study, divided into two groups, 12 in the intervention group with exposure to unpleasant odors, and 12 in the control group without exposure to unpleasant odors.

Using emotion recognition to assess simulation-based learning.

Simulation-based assessment relies on instruments that measure knowledge acquisition, satisfaction, confidence, and the motivation of students. However, the emotional aspects of assessment have not yet been fully explored in the literature. This dimension can provide a deeper understanding of the experience of learning in clinical simulations.