Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Oct 21, 2019
Date Accepted: Jan 26, 2020
An App for Detecting Bullying of Nurses Using the Convolutional Neural Networks and Online Computerized Adaptive Testing: Development and Usability Study
ABSTRACT
Background:
Workplace bullying has been measured in many studies to investigate mental health issues. None uses online computerized adaptive testing (CAT) with bully classifications with convolutional neural networks (CNN) for reporting individual bullied extent at workplace.
Objective:
The aim of this study is to build a model using CNN to develop an app for automatic detection and classification of nurse bullied levels incorporated with online Rasch computerized adaptive testing for helping assess bullying of nurses 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 of suspicious mild, moderate, and severe, respectively) and (2) building a bully prediction model to estimate 69 parameters. Finally, data were separated into training and testing sets in a proportion (70%:30%)—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 bullied level was developed involving the model’s 69 estimated parameters and the online Rasch CAT module for a website assessment.
Results:
We observed that (1) the 22-item model yields higher accuracy rates [0.94 for the total 960 cases and 0.99 and 0.83 for the lower and upper groups with AUCs (0.99; 95% CI 0.99–1.00 and 0.94; 95% CI 0.82–0.99, cutting points it 49 and 66) based on the 947 and 42 cases, respectively]; (2) the 700-case training set with 0.95 accuracies predicts the 260-case testing set reaching an accuracy of 0.97; and (4) an available NAQ-R app for nurses predicting the bullied 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 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. Clinical Trial: Not available
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