Comparison of psychometric properties of the dual-task timed up-and-go test (cognitive) and the 3-m walk backward test in community-dwelling stroke patients.

BACKGROUND: There is a need for practical, easy-to-use and accurately assessing balance tools in stroke patients. OBJECTIVES: This study aimed to compare the psychometric properties of the dual-task Timed Up-and-Go test (cognitive) (DTUG) and the 3-m walk backward test (3MBWT) in stroke patients. METHODS: This study evaluated the practicality, validity, and reliability of the DTUG and the 3MBWT. The test-retest method was used for reliability. The Modified Four Square Step Test (MFSST), the Timed Up-and-Go (TUG), and Berg Balance Scale (BBS) were administered for concurrent validity. A cutoff value was calculated to discriminate between fallers and non-fallers. RESULTS: The mean practicality times of the tests were 63.58 ± 47.32 sec for DTUG and 37.42 ± 24.036 sec for 3MBWT. Intraclass correlation coefficient of the DTUG and 3MBWT were 0.977, 0.964, respectively which showed excellent test - retest reliability. The DTUG demonstrated strong/very strong correlations with the MFSST (r = 0.724, p < 0.001), TUG (r = 0.909, p < 0.001), and BBS (r = -0.740, p < 0.001). The 3MBWT showed strong correlations with the MFSST (r = 0.835, p < 0.001), the TUG (r = 0.799, p < 0.001), and the BBS (r = -0.740, p < 0.001). The cutoff point was 36.945 s for DTUG and 14.605 s for 3MBWT. CONCLUSIONS: The 3MBWT was a more practical test than the DTUG; however, the DTUG was more discriminative than the 3MBWT in identifying fallers after stroke. CLINICAL TRIAL REGISTRATION NUMBER: NCT05211349. URL: https://register.clinicaltrials.gov/prs/app/action/SelectProtocol?sid=S000BRKZ&selectaction=Edit&uid=U0005GRO&ts=2&cx=z21bhg.

The relationship between burnout and work engagement levels of nurses and physiotherapists working during the COVID-19 pandemic period.

BACKGROUND: The COVID-19 pandemic has affected all health professionals worldwide. This has also influenced their working lives, affecting burnout and work engagement. OBJECTIVE: This study aims to investigate the relationship between burnout and work engagement among nurses and physiotherapists during the COVID-19 pandemic. METHODS: This cross-sectional study was conducted with total 509 nurses and physiotherapists who were working at any of the private, public, or university hospitals from two large and one small cities. A Personal Introduction Form, the Maslach Burnout Scale, and the Work Engagement Scale were used in the study. Frequency, percentage, mean, and Pearson correlation analysis were used for statistical analysis. Necessary ethical approvals were taken for the research. RESULTS: There was a significant, moderate, negative relationship between the average scores of the nurses on the vigor and devotion dimensions and the Work Engagement Scale and their average scores on emotional exhaustion, personal accomplishment, depersonalization dimensions and their average score on the Maslach Burnout Scale (p <  0.05). There was a significant, moderate, negative relationship between the scores of the physiotherapists on the Work Engagement Scale and its dimensions and their average scores on the Maslach Burnout Scale and its dimensions (p <  0.05). CONCLUSION: In our study, it was found that the burnout levels of nurses and physiotherapists had an effect on their work engagement during the COVID-19 pandemic. During and after the COVID-19 process, managers should take measures to reduce the burnout levels of health professionals and increase their level of work engagement.

An Out-of-Sample Extension to Manifold Learning via Meta-Modelling.

Unsupervised manifold learning has become accepted as an important tool for reducing dimensionality of a dataset by finding its meaningful low-dimensional representation lying on an unknown nonlinear subspace. Most manifold learning methods only embed an existing dataset, but do not provide an explicit mapping function for novel out-of-sample data, thereby potentially resulting in an ineffective tool for classification purposes, particularly for iterative methods such as active learning. To address this issue, out-of-sample extension methods have been introduced to generalize an existing embedding of new samples. In this work, a novel out-of-sample method is introduced by utilizing High Dimensional Model Representation (HDMR) as a nonlinear multivariate regression with the Tikhonov regularizer for unsupervised manifold learning algorithms. The proposed method was extensively analyzed using illustrative datasets sampled from known manifolds. Several experiments with 3D synthetic datasets and face recognition datasets were also conducted, and the performance of the proposed method was compared to several well-known out-of-sample methods. The results obtained with Locally Linear Embedding (LLE), Laplacian Eigenmaps (LE), and t-Distributed Stochastic Neighbor Embedding (t-SNE) showed that the proposed method achieves competitive even better performance than the other out-of-sample methods.

Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images.

In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands, which might significantly degrade classification performance. In supervised classification, limited training instances in proportion with the number of spectral features have negative impacts on the classification accuracy, which is known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and propose a novel feature-selection algorithm, which is based on the method called high dimensional model representation. The proposed algorithm is tested on some toy examples and hyperspectral datasets in comparison with conventional feature-selection algorithms in terms of classification accuracy, stability of the selected features and computational time. The results show that the proposed approach provides both high classification accuracy and robust features with a satisfactory computational time.