<strong><em>Objective: </em></strong>The purpose of the studies is to analyze and identify the level of competency, which includes knowledge and skills between SCDP nurses (Residence Program) a...<strong><em>Objective: </em></strong>The purpose of the studies is to analyze and identify the level of competency, which includes knowledge and skills between SCDP nurses (Residence Program) and newly graduate nurses (Non-residence) in Saudi Arabia. <strong> <em>Methods: </em></strong>A survey uses an open-ended question conducted among the participant. The data is collected by using tape recording during the interview session. Newly graduate nurses and SCDP nurses were included in this study. (5) Resident nurses vs. (5) non-resident were included in this study making 10 sample size of qualitative study. <strong><em>Results: </em></strong>Findings of the study show significant differences between nonresident and resident in their journey of orientation, competency development and their learning opportunities. Residents are more likely feel less distress and more satisfied about their experience entering the residence program. <em> <strong>Conclusion: </strong></em>Nursing Residency program helps in supporting nurses to build a future leader. Hence, it helps in their critical thinking, skills and knowledge, which elevate their confidence level.展开更多
Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detecti...Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.展开更多
文摘<strong><em>Objective: </em></strong>The purpose of the studies is to analyze and identify the level of competency, which includes knowledge and skills between SCDP nurses (Residence Program) and newly graduate nurses (Non-residence) in Saudi Arabia. <strong> <em>Methods: </em></strong>A survey uses an open-ended question conducted among the participant. The data is collected by using tape recording during the interview session. Newly graduate nurses and SCDP nurses were included in this study. (5) Resident nurses vs. (5) non-resident were included in this study making 10 sample size of qualitative study. <strong><em>Results: </em></strong>Findings of the study show significant differences between nonresident and resident in their journey of orientation, competency development and their learning opportunities. Residents are more likely feel less distress and more satisfied about their experience entering the residence program. <em> <strong>Conclusion: </strong></em>Nursing Residency program helps in supporting nurses to build a future leader. Hence, it helps in their critical thinking, skills and knowledge, which elevate their confidence level.
文摘Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.
