Background: Unemployment is common in multiple sclerosis (MS) and detrimental to quality of life. Studies suggest disclosure of diagnosis is an adaptive strategy for patients. However, the role of cognitive deficits and psychiatric symptoms in disclosure are not well studied.
Objective: The goals of this paper were to (a) determine clinical factors most predictive of disclosure, and (b) measure the effects of disclosure on workplace problems and accommodations in employed patients.
Methods: We studied two overlapping cohorts: a cross-sectional sample (n = 143) to determine outcomes associated with disclosure, and a longitudinal sample (n = 103) compared at four time points over one year on reported problems and accommodations. A case study of six patients, disclosing during monitoring, was also included.
Results: Disclosure was associated with greater physical disability but not cognitive impairment. Logistic regression predicting disclosure status retained physical disability, accommodations and years of employment (p < 0.0001). Disclosed patients reported more work problems and accommodations over time. The case study revealed that reasons for disclosing are multifaceted, including connection to employer, decreased mobility and problems at work.
Conclusion: Although cognitive impairment is linked to unemployment, it does not appear to inform disclosure decisions. Early disclosure may help maintain employment if followed by appropriate accommodations.
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http://dx.doi.org/10.1177/1352458514540971 | DOI Listing |
Neural Netw
January 2025
School of Engineering, Brown University, United States of America; Division of Applied Mathematics, Brown University, United States of America. Electronic address:
Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs).
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
View Article and Find Full Text PDFBiometrics
January 2025
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, United Kingdom.
Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates.
View Article and Find Full Text PDFBJGP Open
January 2025
The Research Unit for General Practice & Section for General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Background: In chronic care, patient-GP collaboration is essential, but might be challenging if patients have complex health problems due to multimorbidity, psychosocial predicaments and addiction problems. To understand and manage these challenges, it is important to explore how patients' and GPs' attempt to collaborate, to maintain and achieve an alliance in order to gain good quality of care.
Aim: To explore how dyads of GPs and patients that GPs deem have complex health problems and difficulties following treatment perceive and manage challenges in their chronic care partnership.
BMC Public Health
January 2025
Institute for Social Marketing and Health, University of Stirling, Stirling, Scotland.
Background: To explore continuities and changes in gambling behaviour during the COVID-19 pandemic and the factors that influenced these among a sample of regular sports bettors.
Methods: A longitudinal qualitative study using in-depth interviews. Sixteen sports bettors living in Britain took part in the first interviews in July-November 2020, and 13 in the follow-up interviews in March-September 2021.
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