The COVID-19 pandemic has prompted higher university lecturers to develop their digital skills in order to adapt to online teaching. A group of university teachers decided to evaluate the educational uses of Mentimeter to promote student participation and active learning. A questionnaire was answered by 400 students and 12 participating academics. These 12 academic respondents also participated in a focus group after experiencing this software during an academic course. Qualitative and quantitative data was collected and analyzed to conclude that this software not only facilitated student participation during the pandemic (both face-to-face and online) in synchronous and asynchronous ways but also improved attention, engagement, collaborative learning and interaction. Immediate feedback made it possible for teachers to monitor the students’ learning processes and to adjust the content and pace accordingly. Students and educators highlighted the inclusive potential of this tool, as it allows participation from a diverse audience with different backgrounds and capacities, ensuring inclusive and equitable education for all. Some opportunities for improvement were also identified, namely more functions to make the software more attractive and adapt it to different educational objectives.

Background: To assess gender differences in the prevalence of self-reported mental disorders, psychological distress and psychotropic drug consumption, and to identify sociodemographic and health-related variables associated with these conditions in the male and female population (aged ≥ 18 years). Methods: A cross-sectional study was carried on 22,141 subjects aged 18 and over, using data from the Spanish National Health Interview Survey 2017. Results: We found an overall prevalence of mental disorders, psychological distress and psychotropic drug consumption of 13.8%, 18.3% and 13.9%, respectively. After multivariable adjustment, women showed significantly increased probabilities of 1.74-fold for mental disorders, 1.26-fold for psychological distress and 1.26-fold for psychotropic drug consumption compared to men. Variables such as gender, age, nationality, marital status, educational level, self-rated health, the presence of different chronic disorders, alcohol consumption and smoking habit were independently associated with mental disorders, psychological distress and psychotropic drug consumption. Several variables showed a differential effect on mental health status and psychotropic drug consumption according to gender. Conclusions: Women suffer from mental disorders, experience psychological distress and consume psychotropic drugs significantly more than men in Spain. Possible explanations for these results may be related to differences in emotional processing, willingness to report diseases and even intrinsic biological traits. Screening for mental health status and psychotropic drug consumption should be considered, particularly in Spanish women, younger adults and individuals who are not married, are obese, have poor self-rated health, suffer from chronic diseases or have a smoking habit.

(1) Background: We examined trends in incidence and outcomes in women with existing type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) and gestational diabetes mellitus (GDM) compared with a control group without diabetes. (2) Methods: This was an observational, retrospective epidemiological study using the National Hospital Discharge Database. (3) Results: There were 2,481,479 deliveries in Spain between 2009 and 2015 (5561 mothers with T1DM, 4391 with T2DM, and 130,980 with GDM). Incidence and maternal age of existing diabetes and GDM increased over time. Women with T2DM were more likely to have obstetric comorbidity (70.12%) than those with GDM (60.28%), T1DM (59.45%), and no diabetes (41.82%). Previous cesarean delivery, preeclampsia, smoking, hypertension, and obesity were the most prevalent risk factors in all types of diabetes. Women with T1DM had the highest rate of cesarean delivery (Risk Ratio (RR) 2.34; 95% Confidence Interval (CI) 2.26–2.43) and prolonged maternal length of stay. Labor induction was higher in T2DM (RR 1.99; 95% CI 1.89–2.10). Women with T1DM had more severe maternal morbidity (RR 1.97; 95% CI 1.70–2.29) and neonatal morbidity (preterm birth, RR 3.32; 95% CI 3.14–3.51, and fetal overgrowth, RR 8.05; 95% CI 7.41–8.75). (4) Conclusions: existing and GDM incidence has increased over time. We found differences in the prevalence of comorbidities, obstetric risk factors, and the rate of adverse obstetric outcomes among women with different types of diabetes. Pregnant women with diabetes have the highest risk of adverse pregnancy outcomes.

(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.

(1) Background: To assess time trends in the incidence and in-hospital outcomes of urinary tract infection (UTI) in older people (≥65 years) in Spain (2001–2018) according to sex and to identify the factors independently associated with in-hospital mortality (IHM). (2) Methods: Using the Spanish National Hospital Database, we analyzed comorbidity, procedures, diagnosis, isolated microorganisms, and outcomes. (3) Results: We included 583,693 admissions (56.76% women). In both sexes, the incidence increased with age over time (p < 0.001). In men and women, the highest IHM was found among the oldest patients (>84 years) with values of 9.16% and 8.6%, respectively. The prevalence of bacteremia increased from 4.61% to 4.98% from 2001 to 2018 (p < 0.001) and sepsis decreased from 3.16% to 1.69% (p < 0.001). The risk of dying increased with age (>84 years) (OR 3.63; 95% CI 3.5–3.78) and with almost all comorbidities. Urinary catheter was a risk factor for women (OR 1.1; 95% CI 1.02–1.18) and protective for men (OR 0.71; 95% CI 0.66–0.76). Isolation of Staphylococcus aureus increased the risk of IHM by 40% among men (OR 1.4; 95% CI 1.28–1.53). After adjusting for confounders, IHM decreased over time. (4) Conclusions: The incidence of UTIs was higher in older patients and increased over time; however, IHM decreased during the same period. Female sex, age, comorbidities, sepsis, and a Staphylococcus aureus isolation increased risk of IHM.

The irruption of artificial intelligence (AI) in all areas of our lives is a reality to which the university, as an institution of higher education, must respond prudently, but also with no hesitation. This paper discusses the potential that resources based on AI presents as potential reviewers of scientific articles in a hypothetical peer review of already published articles. Using different models (GPT-3.5 and GPT-4) and platforms (ChatPDF and Bing), we obtained three full reviews, both qualitative and quantitative, for each of the five articles examined, thus being able to delineate and contrast the results of all of them in terms of the human reviews that these same articles received at the time. The evidence found highlights the extent to which we can and should rely on generative language models to support our decisions as qualified experts in our field. Furthermore, the results also corroborate the hallucinations inherent in these models while pointing out one of their current major shortcomings: the context window limit. On the other hand, the study also points out the inherent benefits of a model that is in a clear expansion phase, providing a detailed view of the potential and limitations that these models offer as possible assistants to the review of scientific articles, a key process in the communication and dissemination of academic research.