Iranian nursing students' perspectives on transition to professional identity: a qualitative study.

AIM: To explore Iranian nursing students' transition to professional identity. BACKGROUND: Professional identity is an important outcome of nursing education that has not been fully explored in the Iranian nursing education system. INTRODUCTION: Professional identity is a significant factor influencing the development of nursing education and practice. The transition of nursing students to professional identity is the main concern of nursing education and fundamental prerequisite for policymaking and planning in the field of nursing education. METHODS: This was a qualitative content analysis study. In-depth unstructured interviews were held with 35 Iranian bachelor's degree nursing students recruited through purposive sampling. The interviews were transcribed verbatim and analysed using content analysis. FINDINGS: The data analysis led to the development of four themes and 15 categories: 'satisfaction with professional practice (attending clinical settings and communicating with patients, the feeling of being beneficial)'; 'personal development (growing interest in nursing, feeling competent in helping others, changing character and attitude shift towards patients)'; 'professional development (realizing the importance of nursing knowledge, appreciating professional roles, a changing their understanding of nursing and the meaning it)'; and 'attaining professional commitment (a tendency to present oneself as a nurse, attempting to change oneself, other students and the public image of nursing)'. DISCUSSION: Development of professional identity is a continual process of transition. The greatest transition occurred in the last year of the programme. CONCLUSION: Nursing students experienced transition to PI through gaining satisfaction with professional practice, undergoing personal and professional development and developing a professional commitment. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Educational policymakers can use our findings for developing strategies that facilitate and support nursing students' transition to professional identity.

Improved diagnostic accuracy for myocardial perfusion imaging using artificial neural networks on different input variables including clinical and quantification data. / Precisión diagnóstica mejorada para la imagen de perfusión miocárdica usando redes neuronales artificiales en diferentes variables de entrada incluyendo datos clínicos y de cuantificación.

OBJECTIVE: Diagnostic accuracy of myocardial perfusion imaging (MPI) is not optimal to predict the result of angiography. The current study aimed at investigating the application of artificial neural network (ANN) to integrate the clinical data with the result and quantification of MPI. METHODS: Out of 923 patients with MPI, 93 who underwent angiography were recruited. The clinical data including the cardiac risk factors were collected and the results of MPI and coronary angiography were recorded. The quantification of MPI polar plots (i.e. the counts of 20 segments of each stress and rest polar plots) and the Gensini score of angiographies were calculated. Feed-forward ANN was designed integrating clinical and quantification data to predict the result of angiography (normal vs. abnormal), non-obstructive or obstructive coronary artery disease (CAD), and Gensini score (≥10 and <10). The ANNs were designed to predict the results of angiography using different combinations of data as follows: reports of MPI, the counts of 40 segments of stress and rest polar plots, and the count of these 40 segments in addition to age, gender, and the number of risk factors. The diagnostic performance of MPI with different ANNs was compared. RESULTS: The accuracy of MPI to predict the result of angiography, obstructive CAD, and Gensini score increased from 81.7% to 92.9%, 65.0% to 85.7%, and 50.5% to 92.9%, respectively by ANN using counts and clinical risk factors. CONCLUSION: The diagnostic accuracy of MPI could be improved by ANN, using clinical and quantification data.