PURPOSE: This purpose of this study was to identify both good teaching and desirable teaching behaviors perceived by nursing students.METHODS: A cross-sectional descriptive design was used. A convenience sample of 324 nursing students was selected and they completed self-reported questionnaires from November 1 to December 30, 2015.RESULTS: Among 4 perspectives of good teaching (traditional, systemic, interaction, and constructionism), the traditional perspective was perceived as the highest form of good teaching, while the systemic perspective was perceived as the lowest. Meanwhile, disclosure and clarity were perceived as the highest desirable teaching behaviors. Regardless of students' perspective of good teaching, all 4 perspectives of good teaching were positively related with clarity, enthusiasm, interaction, organization, and disclosure as desirable teaching behaviors independently.CONCLUSIONS: Nursing students perceived that the highest perspective of good teaching was the traditional perspective. Meanwhile, they perceived that clarity, enthusiasm, interaction, organization, and disclosure were desirable teaching behaviors regardless of their perspective of good teaching. Further study will be needed to perceive nursing faculty's awareness of good teaching and desirable teaching behaviors to identify the difference of awareness between nursing students and faculty.
Disclosure ; Education, Nursing ; Humans ; Nursing ; Students, Nursing

OBJECTIVE: Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. METHODS: Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. RESULTS: Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. CONCLUSION: Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.
Bays ; Forests ; Health Care Sector ; Internet ; Learning ; Machine Learning ; Mental Health ; Residence Characteristics ; Sample Size ; Support Vector Machine