Objectives: This study was conducted to determine how many cancer survivors (CSs) make worksite adjustments and what kinds of adjustments they make. Changes in work ability among employed CSs were explored, and clinical, sociodemographic, and work-related factors associated with the current total work ability were studied.
Methods: CSs of the ten most common invasive types of cancer for men and women in Norway completed a mailed questionnaire 15-39 months after being diagnosed with cancer. Included in the analyses were all participants who worked both at the time of diagnosis and at the time of the survey and who had not changed their labor force status since diagnosis (n = 563). The current total work ability was compared to the lifetime best (0-10 score).
Results: Twenty-six percent of the employed CSs had made adjustments at work, and the most common adjustment was changing the number of work hours per week. Despite the fact that 31% and 23% reported reduced physical and mental work abilities, respectively, more than 90% of the CSs reported that they coped well with their work demands. The mean total work ability score was high (8.6) among both men and women. Being self-employed and working part-time at the time of diagnosis showed significant negative correlations with total work ability, while a favorable psychosocial work environment showed a significant positive correlation. CSs with low work ability were more often in contact with the occupational health service and also made more worksite adjustments than others.
Conclusion: The prospects of future work life seem optimistic for Norwegian employed CSs who return to work relatively soon after primary treatment.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00520-011-1325-3 | 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.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!