The effects of multiple sclerosis (MS) on cognition have gained increasing recognition as one of the major disabling symptoms of the disease. Despite the prevalence of these symptoms and their impact on quality of life, limited attention has been given to strategies that might help manage the cognitive changes commonly experienced by persons with MS. The primary purpose of this study was to determine the effectiveness of a novel computer-assisted cognitive rehabilitation intervention MAPSS-MS (Memory, Attention, Problem Solving Skills in MS) in a multi-site trial with persons with MS. Persons with MS (N = 183) with cognitive concerns were randomly assigned to either the 8-week MAPSS-MS intervention or usual care plus freely available computer games. Participants completed self-report and performance measures of cognitive functioning, compensatory strategies and depression at baseline, immediately after the MAPSS-MS intervention, and three and six months post-intervention. Changes in study outcomes were analyzed using intention to treat methodology, ANOVA with repeated measures, and ANCOVA. Both groups improved significantly on all outcome measures. The intervention group outperformed the comparison group on all measures, and there were statistically significant differences on selected measures. Findings suggest that MAPSS-MS is a feasible intervention that could be broadly implemented in community settings. It has been shown to be modestly successful in improving cognitive functioning.

Background: Person-centered care is widely recognized as a gold standard and is based on a supportive psychosocial climate for both residents and staff in nursing homes. Residents and staff may have different perspectives as to whether the climate in which they interact is person-centered, perhaps due to their different expectations of the nursing home environment and the provision of care services. The aim of this study was to explore and compare resident and staff perspectives of person-centered climate in aged care nursing homes. Methods: This is a descriptive cross-sectional study using a cluster random sampling method. The study collected data in 2016 from residents (n = 251) and nursing staff (n = 249) in 23 nursing homes using a Person-centered Climate Questionnaire-Patient version and Person-centered Climate-Staff version. T-tests for independent-samples were used to compare scores ranked by nursing staff and residents. Results: The mean scores of ‘A climate of safety’ subscale and ‘A climate of everydayness’ subscale rated by residents were significantly lower than those rated by nursing staff. The mean scores of ‘A climate of hospitality’ rated by residents were very low among the three subscales, an indicator of the need to improve a more home-like environment for residents. Residents in larger size nursing homes showed a higher score of person-centered climate compared with their counterparts in small size nursing homes. Conclusions: This study reveals that the perspectives and perceptions of person-centered climate differ between residents and nursing staff. Therefore, both resident and staff perspectives should be taken into account in attempting to improve person-centered climate for better care outcomes.

Problem statement: Seeking Safety (SS) is a widely implemented cognitive‐behavioral therapy for comorbid post‐traumatic stress disorder (PTSD) and substance use disorder (SUD). It is a present‐focused coping skills model that is highly flexible, with varied methods of delivery, to maximize acceptability and client access. The purpose of this meta‐analysis is to examine the effect of SS on comorbid PTSD and SUD across randomized control trials (RCTs). In addition, ours is the first meta‐analysis to examine the dose‐response of SS by comparing delivery of all 25 SS topics versus fewer.Methods and design: Articles published before January 2, 2023 (CINAHL n = 16, PsycINFO n = 31, MEDLINE n = 27, Cochrane n = 38, and Scopus n = 618) were searched. Seven studies were included for meta‐analysis and dose‐response analysis.Results: Based on effect sizes (ES), meta‐analysis revealed that SS has a medium group, time (p = .04), and time by group effect on substance use per the Addiction Severity Index at 3 months and a small effect on Clinician‐Administered PTSD Scale scores by group, a large effect by time, and a medium time by group (p = .002) effect at 6 months. Based on the pooled ES examining various measures across multiple timepoints, SS had small to medium effects on substance use by time, group, or time by group and medium to large effects on PTSD symptoms by time, group, or time by group (except for the group effect at 3‐month follow‐up). Significant effects were found for substance use by time at 3 and 6 months and for PTSD postintervention, at 6 months and 9 months by group, time, and time by group while only by time at 3 months. Meta‐regression revealed that partial dose versions of SS generally function as well as the full dose version of SS when observing long‐term effects (greater than 3 months).Discussion: Findings suggest SS has merit in treating PTSD symptoms and SUD. Based on the summarized effect sizes, SS appears more effective in reducing PTSD than substance use, which converges with the larger treatment outcome literature that consistently finds this. We explore reasons that treatment of SUD is more challenging than treating PTSD and offer suggestions for practitioners. We emphasize the need for future studies to utilize common measures and provide full details of treatment delivery for optimal comparison across studies.

Hospital-acquired pressure injury is one of the most harmful events in clinical settings. Patients who do not receive early prevention and treatment can experience a significant financial burden and physical trauma. Several hospital-acquired pressure injury prediction algorithms have been developed to tackle this problem, but these models assume a consensus, gold-standard label (i.e., presence of pressure injury or not) is present for all training data. Existing definitions for identifying hospital-acquired pressure injuries are inconsistent due to the lack of high-quality documentation surrounding pressure injuries. To address this issue, we propose in this paper an ensemble-based algorithm that leverages truth inference methods to resolve label inconsistencies between various case definitions and the level of disagreements in annotations. Application of our method to MIMIC-III, a publicly available intensive care unit dataset, gives empirical results that illustrate the promise of learning a prediction model using truth inference-based labels and observed conflict among annotators.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.