Abstract
BackgroundMental 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.ObjectiveThis 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.
Highlights
BackgroundMental illness (MI) is common among those who have come in contact with it at the workplace [1] and globally account for 32.4% of years living with a disability [2]
This study aimed to (1) determine featured variables used for convolutional neural networks (CNNs) in the classification of MI, (2) differentiate MI patterns endorsed by participants, and (3) design an MI app for smartphones as a web-based assessment
A total of 107 institutes were targeted according to the hospital list of Taiwan National Health Insurance Administration, and 1521 (753 and 768 in regional hospitals and medical centers, respectively) respiratory therapists (RTs) who were registered in the Taiwan Society for Respiratory Care in January 2019 were included in the study
Summary
BackgroundMental illness (MI) is common among those who have come in contact with it at the workplace [1] and globally account for 32.4% of years living with a disability [2]. Employers are increasingly paying attention to presenteeism—decreased productivity because of health problems—among employees who remain present at work [4] This is because presenteeism might result in a higher economic cost than absenteeism or the medical costs paid by employers [5]. Results: 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. Conclusions: 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
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