Superintendent Office, Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan, Taiwan.
Department of Hospital and Health Care Management, Chia Nan University of Pharmacy and Science, Tainan, Taiwan.
JMIR Mhealth Uhealth. 2020 Jul 31;8(7):e17857. doi: 10.2196/17857.
Mental illness (MI) is common among those who work in health care settings. Whether MI is related to employees' mental status at work is yet to be determined. An MI app is developed and proposed to help employees assess their mental status in the hope of detecting MI at an earlier stage.
This study aims to build a model using convolutional neural networks (CNNs) and fit statistics based on 2 aspects of measures and outfit mean square errors for the automatic detection and classification of personal MI at the workplace using the emotional labor and mental health (ELMH) questionnaire, so as to equip the staff in assessing and understanding their own mental status with an app on their mobile device.
We recruited 352 respiratory therapists (RTs) working in Taiwan medical centers and regional hospitals to fill out the 44-item ELMH questionnaire in March 2019. The exploratory factor analysis (EFA), Rasch analysis, and CNN were used as unsupervised and supervised learnings for (1) dividing RTs into 4 classes (ie, MI, false MI, health, and false health) and (2) building an ELMH predictive model to estimate 108 parameters of the CNN model. We calculated the prediction accuracy rate and created an app for classifying MI for RTs at the workplace as a web-based assessment.
We observed that (1) 8 domains in ELMH were retained by EFA, (2) 4 types of mental health (n=6, 63, 265, and 18 located in 4 quadrants) were classified using the Rasch analysis, (3) the 44-item model yields a higher accuracy rate (0.92), and (4) an MI app available for RTs predicting MI was successfully developed and demonstrated in this study.
The 44-item model with 108 parameters was estimated by using CNN to improve the accuracy of mental health for RTs. An MI app developed to help RTs self-detect work-related MI at an early stage should be made more available and viable in the future.
精神疾病（MI）在医疗保健工作者中很常见。MI 是否与员工的工作心理状态有关，目前尚未确定。开发并提出了一款 MI 应用程序，旨在帮助员工评估其精神状态，以期更早发现 MI。
本研究旨在使用卷积神经网络（CNN）构建模型，并根据措施和装备均方误差的 2 个方面，为使用情感劳动和心理健康（ELMH）问卷自动检测和分类工作场所的个人 MI 建立模型，以便为员工配备一种移动设备上的应用程序来评估和了解自己的精神状态。
我们于 2019 年 3 月招募了 352 名在台湾医疗中心和地区医院工作的呼吸治疗师（RT），填写了 44 项 ELMH 问卷。采用探索性因素分析（EFA）、Rasch 分析和 CNN 作为无监督和监督学习，（1）将 RT 分为 4 类（即 MI、假 MI、健康和假健康），（2）构建 ELMH 预测模型，估计 CNN 模型的 108 个参数。我们计算了预测准确率，并为 RT 开发了一个用于在工作场所分类 MI 的应用程序，作为一种基于网络的评估。
我们观察到，（1）ELMH 中的 8 个领域通过 EFA 保留下来，（2）Rasch 分析将 4 种心理健康（n=6、63、265 和 18 种位于 4 个象限）进行分类，（3）44 项模型的准确率更高（0.92），（4）成功开发并展示了一种用于预测 MI 的 MI 应用程序。
使用 CNN 估计的 44 项包含 108 个参数的模型提高了 RT 心理健康的准确性。未来，应开发更多帮助 RT 尽早自我检测与工作相关的 MI 的 MI 应用程序，使其更可行。
