When the null hypothesis of Friedman’s test is rejected, there is a wide variety of multiple comparisons that can be used to determine which treatments differ from each other. We will discuss the contexts where different multiple comparisons should be applied, when the population follows some discrete distributions commonly used to model count data in biological and ecological fields. Our simulation study shows that sign test is very conservative. Fisher’s LSD and Tukey’s HSD tests computed with ranks are the most liberal. Theoretical considerations are illustrated with data of the Azores Buzzard (Buteo buteo rothschildi) population from Azores, Portugal.

Portugal has the sixth highest road fatality rate among European Union members. This is a problem of different dimensions with serious consequences in people’s lives. This study analyses daily data from police and government authorities on road traffic accidents that occurred between 2016 and 2019 in a district of Portugal. This paper looks for the determinants that contribute to the existence of victims in road traffic accidents, as well as the determinants for fatalities and/or serious injuries in accidents with victims. We use logistic regression models, and the results are compared to the machine-learning model results. For the severity model, where the response variable indicates whether only property damage or casualties resulted in the traffic accident, we used a large sample with a small imbalance. For the serious injuries model, where the response variable indicates whether or not there were victims with serious injuries and/or fatalities in the traffic accident with victims, we used a small sample with very imbalanced data. Empirical analysis supports the conclusion that, with a small sample of imbalanced data, machine-learning models generally do not perform better than statistical models; however, they perform similarly when the sample is large and has a small imbalance.

Background. The mental health of university students has worsened over time, and it is young people who have suffered the most from the COVID-19 pandemic in terms of mental health. Anxiety and depression are the most common symptoms reported by university students and are often the cause of disabilities, either in academic performance or in other spheres of life. Aim. The aim of this study was to both assess the prevalence of depressive and anxiety symptoms in Portuguese university students and analyze the factors associated with these symptoms. Methods. A quantitative cross-sectional study with a sample of 3,399 university students from seven Portuguese higher education institutions was conducted. The following questionnaires were used: the generalized anxiety disorder assessment scale (GAD-7), the patient health questionnaire (PHQ-9), and a study-created sociodemographic questionnaire. The Kendall correlation coefficient, chi-square test of independence, Spearman correlation coefficient, Shapiro-Wilk test, Mann–Whitney-Wilcoxon test, and Kruskall-Wallis test were used to analyze the association between variables. The statistical analysis was done using the software R Statistics (Version 4.0.4), using a significance level of 0.05. Results. Mild to severe anxiety symptoms were reported by 75% of the participants, and 61.2% described mild to severe depressive symptoms. Of the sample, 19.5% reported a previous diagnosis of a mental disorder, with 38.7% diagnosed after the pandemic began. Additionally, 23% reported taking medication for mental health issues, and 26.7% had considered self-harm or harbored thoughts of being “better off dead.” The study found lower anxiety and depressive symptoms ( ) among men, students with higher socioeconomic status, those who frequently traveled home, individuals without prior mental health diagnoses, those with better academic performance, and those who avoided substances like coffee, tobacco, cannabis, and other illegal psychoactive substances. Interestingly, students in romantic relationships exhibited more anxiety symptoms ( ). Moreover, participants who believed they had experienced moral or sexual harassment displayed higher levels of anxiety and depressive symptoms ( ). Conclusions. There was a decrease in the mental health of university students after the pandemic compared to prepandemic studies, and the proportion of students with anxiety and depressive symptoms was alarming. There is an urgent need to implement programs in universities to promote students’ mental health.

The functioning and cognition of older adults can be influenced by different care contexts. We aimed to compare the functioning profiles and cognition of institutionalized and noninstitutionalized older adults and to evaluate the effect of sociodemographic factors on the functioning and cognition. This is a cross-sectional study that included 593 older adults. The data were collected using the Elderly Nursing Core Set and Mini Mental State Examination. Women, older adults who did not attend school and those live in Residential Homes are more likely to have a higher degree of cognitive impairment than men, those who attended school and those frequent Day Centre. The chances of an older adult with moderate or severe cognitive impairment increases with age. Older women, older adults who did not attend school, and older adults who live in Residential Homes had a higher degree of functional problem than men, those who attended school and those who frequent a Day Centre, independently to age. It is necessary to promote the health literacy of older adults throughout life. The implementation of social and health responses should allow older adults to remain in their homes, given the influence of functioning and cognition on self-care and quality of life.

Mental health problems are prevalent among university students, underscoring the need to improve access to effective interventions. Mental healthcare utilization is influenced by general and country-specific factors. This study aimed to identify and compare preferences and barriers to help-seeking for mental health problems among university students in three European countries. A cross-sectional, web-based survey was conducted with a sample of 4960 university students from Portugal (n = 3399), Germany (n = 680), and Sweden (n = 881). Depressive symptoms (9-item Patient Health Questionnaire) and overall mental health (5-item Mental Health Index) were assessed. Students responded to questions regarding help-seeking preferences for and barriers to mental health problems. Rates of moderate to severe depressive symptoms ranged from 37.5 to 51.0 % and were highest among German students. A majority (76.3-89.3 %) of students indicated they would talk to a friend in case of mental health problems. Readiness to seek help was particularly high among Portuguese students. Readiness to use internet-based interventions was low among all students. Up to 51 % of students reported uncertainty regarding where they could seek help. Students with depressive symptoms versus those without were less likely to speak with a friend about emotional problems but were more likely to consider taking medications. Students with depressive symptoms were also more likely to endorse several help-seeking barriers. Reducing structural barriers, improving awareness of treatment options and increasing acceptance of help-seeking for emotional problems may improve uptake of mental health services among students in these European countries.

Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main factors associated with a RTA. The present study aims to contribute to the identification of the determinants for the type of RTA: collision, crash, or pedestrian running-over. These factors are essential for identifying specific countermeasures because there is a relevant relationship between the type of RTA and its severity. Daily RTA data from 2016 to 2019 in a district of Portugal were analyzed. A statistical multinomial logit model was fitted. The identified determinants for the type of RTA were geographical (municipality, location, and parking areas), meteorological (air temperature and weather), time of the day (hour, day of the week, and month), driver’s characteristics (gender and age), vehicle’s features (type and age) and road characteristics (road layout and type). The multinomial model results were compared with several machine learning algorithms, since the original data of the type of RTA are severely imbalanced. All models showed poor performance. However, when combining these models with ROSE for class balancing, their performance improved considerably, with the random forest algorithm showing the best performance.