Objectives: This study aimed to translate and validate the Practice Environment Scale - Nursing Work Index (PES-NWI) among nurses in Indonesia.
Methods: A scale translation and cross-sectional validation study was conducted. The English version was translated into Indonesian, which involved five steps: forward translation, compare the translation, backward translation, compare the translation, and pilot testing with a dichotomous scale (clear or unclear). Thirty inpatient department nurses were involved in checking readability and understandability. A cross-sectional study was conducted from August to October 2022 at 17 hospitals across Indonesia, involving 350 nursing professionals. The validity test included structural validity and convergent validity. The internal consistency reliability was tested by Cronbach's α coefficient, item-total correlation, and composite reliability.
Results: Confirmatory factor analysis (CFA) showed an acceptable fit. The correlation of all dimensions was between 0.70 and 0.88, and all items had item loading higher than 0.6. Convergent validity of each dimension ranged from 0.61 to 0.74, internal consistencies with Cronbach's α coefficient was 0.97, corrected item-to-total correlation ranged from 0.62 to 0.85, and composite reliability of each dimension was higher than 0.89.
Conclusions: Good homogeneity and construct validity have been demonstrated for the Indonesian version of the PES-NWI, nursing management can use it to measure the work environment.
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http://dx.doi.org/10.1016/j.ijnss.2023.09.018 | DOI Listing |
J Med Internet Res
January 2025
AIMS Lab, Center for Neurosciences, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium.
Background: Cognitive deterioration is common in multiple sclerosis (MS) and requires regular follow-up. Currently, cognitive status is measured in clinical practice using paper-and-pencil tests, which are both time-consuming and costly. Remote monitoring of cognitive status could offer a solution because previous studies on telemedicine tools have proved its feasibility and acceptance among people with MS.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
View Article and Find Full Text PDFHealth Rep
January 2025
formerly with the Health Analysis Division, Statistics Canada.
Background: Statistics Canada routinely collects information on functional health and related concepts. Recently, the Washington Group on Disability Statistics (WG) measure of disability has been introduced to the Canadian Community Health Survey (CCHS). The WG measure is used as a tool for developing internationally comparable data on disability.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
School of Software, Taiyuan University of Technology, Jingzhong, China.
Background: The prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive screening tests, prove costly, time-consuming, and invasive, hindering patient compliance and the accessibility of these tests. Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary.
View Article and Find Full Text PDFEJNMMI Phys
January 2025
Department of Nuclear Medicine, Rambam Health Care Campus, P.O.B. 9602, 3109601, Haifa, Israel.
Background: A recently released digital solid-state positron emission tomography/x-ray CT (PET/CT) scanner with bismuth germanate (BGO) scintillators provides an artificial intelligence (AI) based system for automatic patient positioning. The efficacy of this digital-BGO system in patient placement at the isocenter and its impact on image quality and radiation exposure was evaluated.
Method: The digital-BGO PET/CT with AI-based auto-positioning was compared (χ, Mann-Whitney tests) to a solid-state lutetium-yttrium oxyorthosilicate (digital-LYSO) PET/CT with manual patient positioning (n = 432 and 343 studies each, respectively), with results split into groups before and after the date of a recalibration of the digital-BGO auto-positioning camera.
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