In this cross-sectional study, we assessed the feasibility of completing the Patient-Generated Subjective Global Assessment (PG-SGA) in long-stay nursing home residents.
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Malnutrition screening instruments used in hospitals mainly include criteria to identify characteristics of malnutrition. However, to tackle malnutrition in an early stage, identifying risk factors for malnutrition in addition to characteristics may be valuable.The aim of this study was to determine the predictive validity of the Patient-Generated Subjective Global Assessment (PG-SGA SF), which addresses malnutrition characteristics and risk factors, and the Short Nutritional Assessment Questionnaire (SNAQ), which addresses mainly malnutrition characteristics, for length of stay (LOS) in a mixed hospital population.
BACKGROUND: The reliability and validity of the subjective component of the Dutch Objective Burden Inventory (DOBI) are unknown.OBJECTIVE: The validity and reliability of the subjective component of the DOBI were examined in caregivers of individuals with heart failure, using the original 38- and a 24-item version.METHODS: In an online cross-sectional investigation, confirmatory factor analysis was used to examine factorial validity. In examining convergent validity, corrected item-dimension correlations assessed item performance and associations between subjective subscale scores and the Bakas Caregiving Outcomes Scale. Cronbach's α examined internal consistency.RESULTS: The original 4-factor solution was retained and both the original and shorter versions of the subjective component of the DOBI supported adequate construct validity and internal consistency.CONCLUSIONS: Both the 38- and 24-item forms of the subjective DOBI supported construct validity and reliability. Further studies examining the usefulness of both versions are needed in carers of individuals with more severe HF.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
