Development and Validation of an Instrument to Measure Staff Perceptions of Recovery Climate and Culture in Mental Health Programs

Recovery-oriented acute inpatient mental health care: Operationalization and measurement

OBJECTIVE

The current article describes efforts to develop and test a measure of recovery-oriented inpatient care.

METHOD

The Recovery-oriented Acute INpatient (RAIN) scale was based on prior literature and current Veterans Health Administration (VHA) policy and resources and further revised based on data collection from 34 VHA acute inpatient units.

RESULTS

A final scale of 23, behaviorally anchored items demonstrated a four-factor structure including the following factors: inpatient treatment planning, outpatient treatment planning, group programming, and milieu. While several items require additional revision to address psychometric concerns, the scale demonstrated adequate model fit and was consistent with prior literature on recovery-oriented inpatient care. Conclusions and Implementations for Practice: The RAIN scale represents an important tool for future implementation and empirical study of recovery-oriented inpatient care. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

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.