Burnout and psychological distress among Hispanic nurses across Illinois and New York hospitals: Implications for structural and person-centered solutions.

BACKGROUND: Despite high levels of burnout and psychological distress among nurses, few studies have evaluated these outcomes among Hispanic nurses. PURPOSE: To evaluate the differences in job-related and psychological well-being outcomes for Hispanic and non-Hispanic White nurses and the association of nurse work environments. METHODS: Cross-sectional analysis of the 2021 RN4CAST-New York-Illinois nurse survey. Multilevel logistic regression models examined the association between nurse ethnicity and job-related outcomes and psychological well-being. DISCUSSION: Our sample included 798 (10.7%) Hispanic and 6,642 (89.3%) non-Hispanic White nurses in 249 hospitals. In unadjusted models, Hispanic ethnicity was associated with higher odds of burnout (odds ratio (OR) 1.21, 95% confidence interval (CI): 1.03-1.42), which diminished when considering the work environment (OR 1.16, 95% CI: 1.01-1.35) and nurse characteristics (i.e., age) (OR 1.01, 95% CI: 0.83-1.21). CONCLUSION: Equity-driven solutions to support the well-being of Hispanic nurses should consider a focus on the needs of young Hispanic nurses and include increased support in work environments.

Evidence-based practice education in physical medicine and rehabilitation residency programs: A Canadian national survey.

OBJECTIVE: The current extent and quality of evidence based practice (EBP) training for physiatrists is unclear at this time. Training of EBP is also available to residents in Canada. The extent, quality and impact of the training was explored. DESIGN: Cohort study Results: about half of the Canadian programs reported a formal EBP curriculum. The most frequently reported method of providing EBP education were resident participation in journal club. CONCLUSIONS: Despite the increasing integration of EBP into residency program education, there remains a critical lack of knowledge and skills for implementation of EBP into clinical practice among Canadian PM&R residency programs.

A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction.

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.

Wearables for persons with blindness and low vision: form factor matters.

Based on statistics from the WHO and the International Agency for the Prevention of Blindness, an estimated 43.3 million people have blindness and 295 million have moderate and severe vision impairment globally as of 2020, statistics expected to increase to 61 million and 474 million respectively by 2050, staggering numbers. Blindness and low vision (BLV) stultify many activities of daily living, as sight is beneficial to most functional tasks. Assistive technologies for persons with blindness and low vision (pBLV) consist of a wide range of aids that work in some way to enhance one's functioning and support independence. Although handheld and head-mounted approaches have been primary foci when building new platforms or devices to support function and mobility, this perspective reviews potential shortcomings of these form factors or embodiments and posits that a body-centered approach may overcome many of these limitations.

Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development.

Goal: Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability. Methods: We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1-3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). Results: For accuracy in the image center, all approaches had <±2.5 cm average error, except CoreML which had ±5.2-6.2 cm average error at 2-3 meters. In the periphery, all approaches were more inaccurate, with CoreML and IR_self having the highest average errors at ±41 cm and ±32 cm respectively. For usability, CoreML fared favorably with the lowest central processing unit usage, second lowest battery usage, highest field-of-view, and no specialized sensor requirements. Conclusions: We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV.

Low-frequency Motor Cortex EEG Predicts Four Rates of Force Development.

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal that originates from the motor cortex and surrounding cortical regions. As the MRCP reflects both the intention and execution of motor control, it has the potential to serve as a communication interface between patients and neurorehabilitation robots. In this study, we investigated the EEG signal recorded centered at the Cz electrode with the aim of decoding four rates of force development (RFD) during isometric contractions of the tibialis anterior muscle. The four levels of RFD were defined with respect to the maximum voluntary contraction (MVC) of the muscle as follows: Slow (20% MVC/s), Medium (30% MVC/s), Fast (60% MVC/s), and Ballistic (120% MVC/s). Three feature sets were assessed for describing the EEG traces in the classification process. These included: (i) MRCP Morphological Characteristics in the δ-band, such as timing and amplitude; (ii) MRCP Statistical Characteristics in the δ-band, such as standard deviation, mean, and kurtosis; and (iii) Wideband Time-frequency Features in the 0.1-90 Hz range. The four levels of RFD were accurately classified using a support vector machine. When utilizing the Wideband Time-frequency Features, the accuracy was 83% ± 9% (mean ± SD). Meanwhile, when using the MRCP Statistical Characteristics, the accuracy was 78% ± 12% (mean ± SD). The analysis of the MRCP waveform revealed that it contains highly informative data on the planning, execution, completion, and duration of the isometric dorsiflexion task. The temporal analysis emphasized the importance of the δ-band in translating to motor command, and this has promising implications for the field of neural engineering systems.

Patient-specific ascending aortic intervention criteria.

OBJECTIVES: Ascending aortic aneurysms pose a different risk to each patient. We aim to provide personalized risk stratification for such patients based on sex, age, body surface area and aneurysm location (root versus ascending). METHODS: Root and ascending diameters, and adverse aortic events (dissection, rupture, death) of ascending thoracic aortic aneurysm patients were analysed. Aortic diameter was placed in context vis-a-vis the normal distribution in the general population with similar sex, age and body surface area, by conversion to z scores. These were correlated of major adverse aortic events, producing risk curves with 'hinge points' of steep risk, constructed separately for the aortic root and mid-ascending aorta. RESULTS: A total of 1162 patients were included. Risk curves unveiled generalized thresholds of z = 4 for the aortic root and z = 5 for the mid-ascending aorta. These correspond to individualized thresholds of less than the standard criterion of 5.5 cm in the vast majority of patients. Indicative results include a 75-year-old typical male with 2.1 m2 body surface area, who was found to be at increased risk of adverse events if root diameter exceeds 5.15 cm, or mid ascending exceeds 5.27 cm. An automated calculator is presented, which identifies patients at high risk of adverse events based on sex, age, height, weight, and root and ascending size. CONCLUSIONS: This analysis exploits a large sample of aneurysmal patients, demographic features of the general population, pre-dissection diameter, discrimination of root and supracoronary segments, and statistical tools to extract thresholds of increased risk tailor-made for each patient.

Evaluating the efficacy of UNav: A computer vision-based navigation aid for persons with blindness or low vision.

UNav is a computer-vision-based localization and navigation aid that provides step-by-step route instructions to reach selected destinations without any infrastructure in both indoor and outdoor environments. Despite the initial literature highlighting UNav's potential, clinical efficacy has not yet been rigorously evaluated. Herein, we assess UNav against standard in-person travel directions (SIPTD) for persons with blindness or low vision (PBLV) in an ecologically valid environment using a non-inferiority design. Twenty BLV subjects (age = 38 ± 8.4; nine females) were recruited and asked to navigate to a variety of destinations, over short-range distances (<200 m), in unfamiliar spaces, using either UNav or SIPTD. Navigation performance was assessed with nine dependent variables to assess travel confidence, as well as spatial and temporal performances, including path efficiency, total time, and wrong turns. The results suggest that UNav is not only non-inferior to the standard-of-care in wayfinding (SIPTD) but also superior on 8 out of 9 metrics, as compared to SIPTD. This study highlights the range of benefits computer vision-based aids provide to PBLV in short-range navigation and provides key insights into how users benefit from this systematic form of computer-aided guidance, demonstrating transformative promise for educational attainment, gainful employment, and recreational participation.