Congress:
EuroSafe Imaging 2021
Keywords:
Radiographers, Radioprotection / Radiation dose, Experimental, Radiation safety, Workforce
Authors:
S. Maharjan, K. Parajuli, S. Sah, U. Poudel
DOI:
10.26044/esi2021/ESI-10710
Methods or background
Questionnaire
A questionnaire survey was performed to obtain a snapshot of knowledge of radiation protection among radiology professionals. The survey included demographic characteristics (age, gender, academic qualification, and work experience) and multiple-choice questions (MCQ) related to radiation protection. 17 questions were administered to each participant, 3 questions were related to general information regarding training, knowledge, and experience of medical radiation imaging. The remaining 14 multiple choice questions (MCQ) measured the level of understanding of radiation protection. The questionnaire survey was conducted at the Department of Radio-Diagnosis & Medical Imaging, Universal College of Medical Sciences (UCMS), Bhairahawa, Nepal.
Data Collection
All the staff and students of the Department of Radio-Diagnosis & Medical Imaging participated in the survey. The data were collected from 1st to 10th October 2015. The department comprises of Radiologist, Medical Physicist, Medical Imaging faculty, Radiologic Technologist, Radiographers, MD Radio-diagnosis residents, and undergraduate students of medical imaging technology. Participants were handed out the hardcopy questionnaire survey by the principal investigator himself and were requested to complete in front of the investigator. Each correct answer was given “1″ score and for negative answers, there were no negative markings.
Data Analysis
Data were inserted into SPSS statistical software, version 27, IBM, Chicago, United States. A descriptive analysis and statistical tests were performed. The knowledge of radiation protection was categorized as inadequate <60 %; adequate 60–80 % and excellent 80–100 %. The normality of the data was checked by using the Shapiro-Wilk test. The equality of variance was assessed by Levene’s test. Non-parametric tests, namely Mann-Whitney U-test and Kruskal-Wallis H-test were used for statistical analysis since the data did not follow the normal distribution. Pairwise post-hoc test with Bonferroni correction was applied for statistically significant findings obtained from Kruskal-Wallis H-test. The p-value≤0.05 was considered as statistically significant.
Principal Component Analysis (PCA) was used to reduce 14 dimensions (questions) into a few principal components. The first two components were selected that explained 49.1 % of the total variance. PCA was applied using prcomp() function in RStudio, an integrated development environment (IDE) for R programming language, Boston, Massachusetts, United States and the first two components having higher eigenvalues were visualized using “ggbiplot” library[9]. Scree plot was conducted to extract the number of principal components to be analyzed. The scree plot is shown in Fig 1. The eigenvalue determines the magnitude of correlation.