Objective: Previous work ability studies have primarily focused on old workers and physical health. This study investigated how poor perceived work ability (PPWA) is associated with work-related factors in different health and social service (HSS) worker age groups.
Design: Cross-sectional survey in 2020.
Setting: HSS employees (general HSS and eldercare) in nine Finnish public sector organisations.
Participants: All employees who were employed in the organisation completed self-reported questionnaires. Of the original sample (N=24 459, response rate 67%), 22 528 gave consent for research use.
Primary And Secondary Outcome Measures: Participants evaluated their psychosocial work environment and work ability. Lowest decile of work ability was categorised as poor. The association between psychosocial work-related factors and PPWA in different age-groups of HSS workers, adjusting for perceived health, was analysed with logistic regression.
Results: The proportion of PPWA was highest in shift workers, eldercare employees, practical nurses and registered nurses. Considerable variation between age groups exists in the work-related psychosocial factors associated with PPWA. Among young employees engaging leadership and working time and work task autonomy were statistically significant, whereas in middle-aged and old employees procedural justice and ethical strain were highlighted. The strength of the association with perceived health also differs in age groups (young: OR=3.77, 95% CI 3.30 to 4.30; middle-aged: OR=4.66, 95% CI 4.22 to 5.14; old: OR=6.16, 95% CI 5.20 to 7.18).
Conclusions: Young employees would benefit from engaging leadership and mentoring, and from more working time and work task autonomy. As employees get older they would benefit more from job modification and from ethical and just organisation culture.
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http://dx.doi.org/10.1136/bmjopen-2022-066506 | DOI Listing |
Atten Percept Psychophys
January 2025
Department of Psychology, The Ohio State University, 225 Psychology Building, 1835 Neil Ave, Columbus, OH, 43210, USA.
Humans can learn to attentionally suppress salient, irrelevant information when it consistently appears at a predictable location. While this ability confers behavioral benefits by reducing distraction, the full scope of its utility is unknown. As people locomote and/or shift between task contexts, known-to-be-irrelevant locations may change from moment to moment.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.
View Article and Find Full Text PDFJ Occup Rehabil
January 2025
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Purpose: We aimed to develop an online vocational rehabilitation (VR) readiness screening (VRRS) tool for young adults diagnosed with cancer. VR readiness was defined as being physically and cognitively ready to enter or return to work or school.
Methods: We developed an initial VRRS tool informed by previous studies, a scoping review to determine such a tool had not already been developed, and consultation with subject matter experts.
Cogn Process
January 2025
Institute of Cognitive Sciences and Technologies (ISTC-CNR), Via Nomentana 56, 00161, Rome, Italy.
Face masks can impact processing a narrative in sign language, affecting several metacognitive dimensions of understanding (i.e., perceived effort, confidence and feeling of understanding).
View Article and Find Full Text PDFSci Rep
January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
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