Determining professionalism in Turkish students nurses

Characteristics of the nursing profession include educational standards, professional organizations, commitment, autonomy, continuing education, body of knowledge and competencies, social value, and a code of ethics. This study was carried out with the aim of determining the professional attitudes of nursing students in Turkey. It was a descriptive study. This study was conducted in 25 nursing schools that provide graduate level nursing education in Turkey. The sample of the study included 1412 final year nursing students who were selected by random sampling from nursing schools offering education at bachelor level. Data was collected using a questionnaire, which included demographic characteristics of students and an Inventory to Measure Professional Attitudes in Student Nurses (IPASN). The mean score of IPASN was 4.1 ± 0.5 and the areas the highest mean scores were for autonomy, competence and continuous education whilst lowest ones were for  cooperation, contribution to scientific knowledge, and participating in professional organizations. In conclusion, the overall mean scores of professional attitudes for nursing students were found to be satisfying and some recommendations were made to improve subgroups scores

Towards an international language for incontinence-associated dermatitis (IAD) : design and evaluation of psychometric properties of the Ghent Global IAD Categorization Tool (GLOBIAD) in 30 countries

Background: Incontinence-associated dermatitis (IAD) is a specific type of irritant contact dermatitis with different severity levels. An internationally accepted instrument to assess the severity of IAD in adults, with established diagnostic accuracy, agreement and reliability, is needed to support clinical practice and research. Objectives: To design the Ghent Global IAD Categorization Tool (GLOBIAD) and evaluate its psychometric properties. Methods: The design was based on expert consultation using a three-round Delphi procedure with 34 experts from 13 countries. The instrument was tested using IAD photographs, which reflected different severity levels, in a sample of 823 healthcare professionals from 30 countries. Measures for diagnostic accuracy (sensitivity and specificity), agreement, interrater reliability (multirater Fleiss kappa) and intrarater reliability (Cohen's kappa) were assessed. Results: The GLOBIAD consists of two categories based on the presence of persistent redness (category 1) and skin loss (category 2), both of which are subdivided based on the presence of clinical signs of infection. The agreement for differentiating between category 1 and category 2 was 0.86 [95% confidence interval (CI) 0.86-0.87], with a sensitivity of 90% and a specificity of 84%. The overall agreement was 0.55 (95% CI 0.55-0.56). The Fleiss kappa for differentiating between category 1 and category 2 was 0.65 (95% CI 0.65-0.65). The overall Fleiss kappa was 0.41 (95% CI 0.41-0.41). The Cohen's kappa for differentiating between category 1 and category 2 was 0.76 (95% CI 0.75-0.77). The overall Cohen's kappa was 0.61 (95% CI 0.59-0.62). Conclusions: The development of the GLOBIAD is a major step towards a better systematic assessment of IAD in clinical practice and research worldwide. However, further validation is needed. What's already known about this topic? - Incontinence-associated dermatitis (IAD) is an irritant contact dermatitis in adults with incontinence. - Ten IAD severity categorization instruments have been developed, some of which have been found to be time-consuming and (linguistically) complex when used in clinical practice. - A universal IAD classification system is needed to guide practice, inform educational platforms and support research. What does this study add? - The Ghent Global IAD Categorization Tool is based on input from international experts and was psychometrically tested by 823 healthcare professionals from 30 countries. - The accuracy of differentiating between a diagnosis of erythema vs. skin loss was high when IAD was classified based on images. - The identification of clinical signs of infection can be prone to error