R you ready? Using the R programme for statistical analysis and graphics

© 2019 Wiley Periodicals, Inc. For conducting research, nurses typically use commercial statistical packages. R software is a free, powerful, and flexible alternative, but is less familiar and used less frequently in nursing research. In this paper, we use data from a previous study to demonstrate a few typical steps in exploratory data analysis using R. A step-by-step description of some basic analyses in R is provided here, including examples of specific functions to read and manipulate the data, calculate scores from individual questionnaire items, and prepare a correlation plot and summary table

Using Content Validity for the Development of Objective Structured Clinical Examination Checklists in a Slovenian Undergraduate Nursing Program

Introduction: The objective structured clinical examination (OSCE) has been adopted by many universities for the assessment of healthcare competencies and as a formative teaching tool in both undergraduate and postgraduate nursing education programs. This pilot study evaluates the validity of OSCE checklists to be used in first‐year undergraduate nurse practice education

An international cross-cultural study of nursing student's perceptions of caring

© 2019 Elsevier Ltd Background: Single studies suggest that nursing students perceive caring as more an instrumental than expressive behaviour and indicate some differences between caring perceptions in junior and senior nursing students. However, there are limited studies investigating caring perceptions in nursing students across multiple cultures. Objective: To determine perceptions of caring in Slovene, Croatian, Chinese and Russian nursing students and explore whether there are statistically significant differences in perceptions of caring between countries and between first and third-year nursing students. Design: A cross-sectional descriptive study design was used. Settings and participants: The study included 604 nursing students enrolled in first and third year in seven different nursing faculties in four countries: Slovenia; China; Croatia; and the Russian Federation. Methods: The 25-item Caring Dimension Inventory (CDI-25) was used to measure caring perceptions. We also included demographic questions regarding age, gender, country, year of study and type of study. Demographic data were analysed using descriptive analysis while a two-way analysis of variance (ANOVA) adjusted for unequal sample sizes was performed together with a post hoc analysis of the results. Results: The results of two-way ANOVA showed that both main effects (country and year of study) were statistically significant, as well as their interaction at the 0.05 significance level. The main effect for country was F(3, 596) = 3.591, p < 0.0136 indicating a significant difference in CDI-25 between Slovenia (M = 108.9, SD = 9.2), Russian Federation (M = 107.1, SD = 8.2), China (M = 102.8, SD = 9.7) and Croatia (M = 110.0, SD = 8.6). Conclusions: Perceptions of caring in nursing students differ across countries, probably due to different educational systems, curricula, cultural differences and societal values. Implementing caring theories in nursing curricula could help students to cultivate caring during their education

Impact of Education, Working Conditions, and Interpersonal Relationships on Caregivers’ Job Satisfaction

Aim: To explore relationships between caregivers’ education, healthcare working conditions, interpersonal relationships, and caregivers’ general job satisfaction

Evaluation of major online diabetes risk calculators and computerized predictive models

Classical paper-and-pencil based risk assessment questionnaires are often accompanied by the online versions of the questionnaire to reach a wider population. This study focuses on the loss, especially in risk estimation performance, that can be inflicted by direct transformation from the paper to online versions of risk estimation calculators by ignoring the possibilities of more complex and accurate calculations that can be performed using the online calculators. We empirically compare the risk estimation performance between four major diabetes risk calculators and two, more advanced, predictive models. National Health and Nutrition Examination Survey (NHANES) data from 1999%2012 was used to evaluate the performance of detecting diabetes and pre-diabetes. American Diabetes Association risk test achieved the best predictive performance in category of classical paper-and-pencil based tests with an Area Under the ROC Curve (AUC) of 0.699 for undiagnosed diabetes (0.662 for pre-diabetes) and 47% (47% for pre-diabetes) persons selected for screening. Our results demonstrate a significant difference in performance with additional benefits for a lower number of persons selected for screening when statistical methods are used. The best AUC overall was obtained in diabetes risk prediction using logistic regression with AUC of 0.775 (0.734) and an average 34% (48%) persons selected for screening. However, generalized boosted regression models might be a better option from the economical point of view as the number of selected persons for screening of 30% (47%) lies significantly lower for diabetes risk assessment in comparison to logistic regression (p < 0.001), with a significantly higher AUC (p < 0.001) of 0.774 (0.740) for the pre-diabetes group. Our results demonstrate a serious lack of predictive performance in four major online diabetes risk calculators. Therefore, one should take great care and consider optimizing the online versions of questionnaires that were primarily developed as classical paper questionnaire