An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study

doi:10.2196/16747

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24849

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/24849

Title:	An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study
Authors:	Ma, SC;Chou, W;Chien, TW;Chow, JC;Yeh, YT;Chou, PH;Lee, HF
Keywords:	nurse bullying;NAQ-R assessment;receiver operating characteristic curve;convolutional neural network;computerized adaptive testing
Date:	2020
Issue Date:	2022-08-09T08:08:59Z (UTC)
Publisher:	JMIR PUBLICATIONS, INC
ISSN:	2291-5222
Abstract:	Background: Workplace bullying has been measured in many studies to investigate its effects on mental health issues. However, none have used web-based computerized adaptive testing (CAT) with bully classifications and convolutional neural networks (CNN) for reporting the extent of individual bullying in the workplace. Objective: This study aims to build a model using CNN to develop an app for automatic detection and classification of nurse bullying-levels, incorporated with online Rasch computerized adaptive testing, to help assess nurse bullying at an earlier stage. Methods: We recruited 960 nurses working in a Taiwan Ch-Mei hospital group to fill out the 22-item Negative Acts Questionnaire-Revised (NAQ-R) in August 2012. The k-mean and the CNN were used as unsupervised and supervised learnings, respectively, for: (1) dividing nurses into three classes (n=918, 29, and 13 with suspicious mild, moderate, and severe extent of being bullied, respectively); and (2) building a bully prediction model to estimate 69 different parameters. Finally, data were separated into training and testing sets in a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve [AUC]), along with the accuracy across studies for comparison. An app predicting the respondent bullying-level was developed, involving the model's 69 estimated parameters and the online Rasch CAT module as a website assessment. Results: We observed that: (1) the 22-item model yields higher accuracy rates for three categories, with an accuracy of 94% for the total 960 cases, and accuracies of 99% (AUC 0.99; 95% CI 0.99-1.00) and 83% (AUC 0.94; 95% CI 0.82-0.99) for the lower and upper groups (cutoff points at 49 and 66 points) based on the 947 cases and 42 cases, respectively; and (2) the 700-case training set, with 95% accuracy, predicts the 260-case testing set reaching an accuracy of 97. Thus, a NAQ-R app for nurses that predicts bullying-level was successfully developed and demonstrated in this study. Conclusions: The 22-item CNN model, combined with the Rasch online CAT, is recommended for improving the accuracy of the nurse NAQ-R assessment. An app developed for helping nurses self-assess workplace bullying at an early stage is required for application in the future.
URI:	http://dx.doi.org/10.2196/16747 https://www.webofscience.com/wos/woscc/full-record/WOS:000534231100001 https://ir.csmu.edu.tw:8080/handle/310902500/24849
Relation:	JMIR MHEALTH AND UHEALTH ,2020 ,v8 ,issue 5
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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