Hasil untuk "Vocational rehabilitation. Employment of people with disabilities"

Hanri Kriel, Maria M. Kriel-Kruger

No abstract available.

Riccardo Volpato, Simone Stumpf, Lisa DeBruine

People are increasingly turning to generative AI (e.g., ChatGPT, Gemini, Copilot) for emotional support and companionship. While trust is likely to play a central role in enabling these informal and unsupervised interactions, we still lack an understanding of how people develop and experience it in this context. Seeking to fill this gap, we recruited 24 frequent users of generative AI for emotional support and conducted a qualitative study consisting of diary entries about interactions, transcripts of chats with AI, and in-depth interviews. Our results suggest important novel drivers of trust in this context: familiarity emerging from personalisation, nuanced mental models of generative AI, and awareness of people's control over conversations. Notably, generative AI's homogeneous use of personalised, positive, and persuasive language appears to promote some of these trust-building factors. However, this also seems to discourage other trust-related behaviours, such as remembering that generative AI is a machine trained to converse in human language. We present implications for future research that are likely to become critical as the use of generative AI for emotional support increasingly overlaps with therapeutic work.

Sangjun Eom, Tianyi Hu, Wenyi Xu et al.

Early mobilization is a structured protocol designed to facilitate motor recovery in intensive care unit (ICU) patients with ICU-acquired weakness. This process is typically implemented by an interdisciplinary team of nurses, physical therapists, and other healthcare professionals. However, its application is often constrained by the patients' critical conditions, limited mobility, and the challenges of coordinating care within resource-intensive ICU environments. In this study, we developed a patient-centered virtual reality (VR) exergame through an interdisciplinary design process involving clinicians and therapists, tailored to the constraints of critical care. The exergame incorporates progressive mobility levels that mirror early mobilization practices, and includes an embodied avatar to provide guidance and motivation. Using Meta Quest 3 body tracking, the system captures and visualizes patients' movements, thereby providing motivational engagement and quantifiable mobility metrics. We evaluated the exergame in two stages: a dual-user study involving healthy participants and healthcare professionals or students (N = 13), and a subsequent study with cardiothoracic ICU patients (N = 18) to assess feasibility, design validity, and clinical acceptance. Across both studies, participants reported high enjoyment and engagement without discomfort or stress. Furthermore, patients demonstrated increases in movement speed, range of motion, and workspace volume of the upper body across game levels. Physiological monitoring further indicated that the exergame elicited exertion without inducing excessive cardiovascular responses. These findings highlight the feasibility of VR exergames as a clinically acceptable and engaging adjunct to early mobilization in critical care, offering a novel pathway to improve rehabilitation outcomes for ICU patients.

Sorcha Walsh, Vlad Glaveanu

Attention Deficit Hyperactivity Disorder (ADHD) is a neuro-behavioural condition associated with limitations and deficits. Previous literature has largely focused on the lived experiences of males with ADHD. Despite a recent increase in studies on females with ADHD, there is a lack of empirical research on school experiences. This study aimed to address this gap in the literature by examining the perceived effect of ADHD on the school experiences of women. 13 women aged between 18–35 with a medical diagnosis of ADHD answered a series of questions in qualitative semi-structured interviews. Three were diagnosed during school and ten were diagnosed after school. The data was analysed using structured inductive thematic analysis. Four themes emerged, with sub-themes as follows: (1) Personal impact; the impact ADHD had on self-identity in school pre- and post-diagnosis. (2) Attitudes and opinions of others; how parents, teachers and societal attitudes on gender affected diagnoses and school experiences. (3) Friendships and relationships; how ADHD affected personality and socialising in school, and (4) ADHD and academia; the academic struggles participants faced whether diagnosed in school or not. The results suggest that ADHD contributed to negative school experiences for all participants. While the perception was that gender and attitudes towards ADHD in females often delayed diagnoses and that early diagnosis would have improved self-identity and school experience, results suggest even with early diagnosis, it is important that teachers are educated about the symptoms and deficits affecting females with ADHD and that schools provide resources to support female students with ADHD to ensure the best school experience.

Allison Chen, Sunnie S. Y. Kim, Angel Franyutti et al.

How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we randomly assigned participants (N = 470) to watch short informational videos presenting LLMs as either machines, tools, or companions -- or to watch no video. We then assessed how strongly they believed LLMs to possess various mental capacities, such as the ability to have intentions or remember things. We found that participants who watched video messages presenting LLMs as companions reported believing that LLMs more fully possessed these capacities than did participants in other groups. In a follow-up study (N = 604), we replicated these findings and found nuanced effects on how these videos also impact people's reliance on LLM-generated responses when seeking out factual information. Together, these studies suggest that messages about LLMs -- beyond technical advances -- may shape what people believe about these systems and how they rely on LLM-generated responses.

Mohammed M. Alshehri, Vandana Esht, Ramzi Abdu Alajam et al.

Transcranial direct current stimulation (tDCS) is known to modulate the brain excitability and enhance cognitive functions and neuroplasticity, although adherence to its administration in post-stroke rehabilitation is still being understudied. This study set out to develop and content validate a questionnaire to assess clinical physiotherapists’ knowledge, attitude, and practices regarding the use of tDCS following a stroke. In order to gather expert opinion and come to a consensus on a certain topic, the Delphi technique was employed. The measure was validated using both qualitative (cognitive interviewing) and quantitative (content validity) methods on a panel of 32 experts. Calculations were made for the content validity ratio (CVR), content validity index (CVI), item-level CVI (I-CVI), and scale-level CVI universal agreement (S-CVI/UA). A total of 48 interdisciplinary experts in the field of neurosciences were invited. In all, 32 specialists from Neurology, General Medicine, Neurophysiotherapy, and Physiology departments accepted the invitation and provided their opinion for instrument analysis. After two rounds, early iterations of this instrument demonstrated an acceptable CVR value of 1, high overall content validity (S-CVI/UA = 0.86), and high content validity of individual items (I-CVI range: 0.83-1.00). The kappa value varied between 0.75 and 1, which is excellent. Its content is therefore deemed validated. Through an iterative process, its development and assessment revealed strong item-content validity for determining the domains of the questionnaire. It is anticipated that this metric could be utilized to increase the adherence rate of post-stroke tDCS application in Saudi Arabia.

Mary Beth Bruder, PhD, Tara M. Lutz, PhD, Kelly E. Ferreira, PhD

It is estimated that 25% of adults in the US are living with one or more disabilities. Persons with disabilities (PWD) have unmet health care needs, experience health disparities compared to those without disabilities. Barriers to quality health care include structural barriers such as access to transportation, physical access to facilities, and lack of accessible medical equipment. Other barriers include the knowledge, skills, beliefs, and attitudes of health care providers to meet the needs of PWD. To examine the barriers to health care experienced by adults with disabilities in Connecticut, the University of Connecticut Center for Excellence in Developmental Disabilities and Leadership Education in Neurodevelopmental and related Disabilities program (UConn UCEDD; UConn LEND) worked with self-advocates to develop a 25-question online survey in plain language. Data from 78 PWD and/or family members and caregivers of a PWD living in Connecticut were analyzed. Respondents reported experiencing issues scheduling appointments due to lack of availability, challenges with the scheduling system, or transportation difficulties (21%), using devices for telehealth visits (27%), and insurance coverage and cost (17%). More than half of respondents reported needing accessible buildings and exam rooms as well as accessible medical diagnostic equipment (MDE). Finally, 43% of respondents reported problems communicating with health care providers, 39% reported that providers demonstrated negative attitudes towards them during visits, and 61% reported that they did not think their health care providers had enough training about working with PWD. The results of this survey suggest that PWD living in Connecticut experience similar barriers to health care access as PWD across the country. Our findings support the need for federal enforcement of standards for accessible MDE, state-level compliance of the standards, and required training in disability competency for all health care training programs to ensure that providers can meet the needs of PWD.

David Haag, Devender Kumar, Sebastian Gruber et al.

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual's current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing.

Lennart Wachowiak, Andrew Coles, Oya Celiktutan et al.

Large language models (LLMs) are increasingly used in robotics, especially for high-level action planning. Meanwhile, many robotics applications involve human supervisors or collaborators. Hence, it is crucial for LLMs to generate socially acceptable actions that align with people's preferences and values. In this work, we test whether LLMs capture people's intuitions about behavior judgments and communication preferences in human-robot interaction (HRI) scenarios. For evaluation, we reproduce three HRI user studies, comparing the output of LLMs with that of real participants. We find that GPT-4 strongly outperforms other models, generating answers that correlate strongly with users' answers in two studies $\unicode{x2014}$ the first study dealing with selecting the most appropriate communicative act for a robot in various situations ($r_s$ = 0.82), and the second with judging the desirability, intentionality, and surprisingness of behavior ($r_s$ = 0.83). However, for the last study, testing whether people judge the behavior of robots and humans differently, no model achieves strong correlations. Moreover, we show that vision models fail to capture the essence of video stimuli and that LLMs tend to rate different communicative acts and behavior desirability higher than people.

Abeer Badawi, Somayya Elmoghazy, Samira Choudhury et al.

Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with a diminished quality of life. Agitation and aggression (AA) in people living with dementia (PwD) contribute to distress and increased healthcare demands. Current assessment methods rely on caregiver intervention and reporting of incidents, introducing subjectivity and bias. Artificial Intelligence (AI) and predictive algorithms offer a potential solution for detecting AA episodes in PwD when utilized in real-time. We present a 5-year study system that integrates a multimodal approach, utilizing the EmbracePlus wristband and a video detection system to predict AA in severe dementia patients. We conducted a pilot study with three participants at the Ontario Shores Mental Health Institute to validate the functionality of the system. The system collects and processes raw and digital biomarkers from the EmbracePlus wristband to accurately predict AA. The system also detected pre-agitation patterns at least six minutes before the AA event, which was not previously discovered from the EmbracePlus wristband. Furthermore, the privacy-preserving video system uses a masking tool to hide the features of the people in frames and employs a deep learning model for AA detection. The video system also helps identify the actual start and end time of the agitation events for labeling. The promising results of the preliminary data analysis underscore the ability of the system to predict AA events. The ability of the proposed system to run autonomously in real-time and identify AA and pre-agitation symptoms without external assistance represents a significant milestone in this research field.

Pratim Guha Niyogi, Muraleetharan Sanjayan, Dmitri Volfson et al.

Research on modeling the distributional aspects in sensor-based digital health (sDHT) data has grown significantly in recent years. Most existing approaches focus on using individual-specific density or quantile functions. However, there has been limited exploration to assess the practical utility of alternative distributional representations in clinical contexts collecting sDHT data. This study is motivated by accelerometry data collected on 246 individuals with multiple sclerosis (MS)representing a wide range of disability (Expanded Disability Status Scale, EDSS: 0-7). We consider five different individual-level distributional representations of minute-level activity counts: density, survival, hazard, quantile, and total time on test functions. For each of the five distributional representations, scalar-on-function regression fits linear discriminators for binary and continuously measured MS disability, and cross-validated discriminatory performance of these linear discriminators is compared across. The results show that individual-level hazard functions provide the highest discriminatory accuracy, more than double the accuracy compared to density functions. Individual-level quantile functions provided the second-highest discriminatory accuracy. These findings highlight the importance of focusing on distributional representations that capture the tail behavior of distributions when analyzing digital health data, especially in clinical contexts.

Kathrin Gerling, Anna-Lena Meiners, Louisa Schumm et al.

Creating accessible Virtual Reality (VR) is an ongoing concern in the Human-Computer Interaction (HCI) research community. However, there is little reflection on how accessibility should be conceptualized in the context of an experiential technology. We address this gap in our work: We first explore how accessibility is currently defined, highlighting a growing recognition of the importance of equitable and enriching experiences. We then carry out a literature study (N=28) to examine how accessibility and its relationship with experience is currently conceptualized in VR research. Our results show that existing work seldom defines accessibility in the context of VR, and that barrier-centric research is prevalent. Likewise, we show that experience - e.g., that of presence or immersion - is rarely designed for or evaluated, while participant feedback suggests that it is relevant for disabled users of VR. On this basis, we contribute a working definition of VR accessibility that considers experience a necessary condition for equitable access, and discuss the need for future work to focus on experience in the same way as VR research addressing non-disabled persons does.

usama umer, Syed Hammad Mian, Khaja Moiduddin et al.

Three-dimensional (3D) printing, especially using fused deposition modeling, is becoming more and more popular in the medical sector because of its exceptional advantages. While it has been used for prototyping, 3D printing has not yet been completely explored to produce a functional product. The key causes are the abundance of 3D printing materials and the lack of a comprehensive study outlining the design process. Consequently, this paper describes a reverse engineering (RE) design approach based on data acquisition utilizing laser scanning and splint design from the acquired point cloud data. This study also focuses on the evaluation of various wrist orthosis/splint designs and materials using finite element (FE) analysis in order to improve upon the conventional approach. Sixty FE analysis simulations are undertaken in flexion–extension and radial–ulnar wrist movements to investigate the displacements and the stresses. The splint is then fabricated utilizing the material and thickness that have been specified by FE analysis. The major goals of this study are to examine the RE design methodology, explore various materials, and assess the viability of 3D printing. The polylactic acid (PLA) hand splint has proven to be the sturdiest in terms of average displacements when compared to the other materials, followed by polyethylene terephthalate glycol (PETG), acrylonitrile butadiene styrene (ABS), polypropylene, and thermoplastic polyurethanes. According to simulation data, the PLA splint has 38.6%, 38.8%, 38.5%, and 38.7% less displacement in the major loading direction in flexion, extension, radial, and ulnar, respectively, than the ABS splint. Moreover, the PLA-based hand splint has a peak stress value below the yield strength of PLA, rendering it reliable for patients to wear. Also, it turns out that PETG and ABS behave rather similarly. Furthermore, it has been shown that a balanced approach can reduce material use and building time. For instance, employing PLA and a thickness of 2 mm results in reduced material costs without compromising the effectiveness of the splint. As a result, choosing the right material and splint thickness can help the 3D-printed hand splint perform better.

Nathan J. Rabang, Amy E. West, Eric Kurtz et al.

Populations researched often have little if any input in the means of data collection, analysis, or authorship of the findings published. They are excluded from participating in the scientific methods even though they are the subject of the content that is being produced. This is true for Indigenous populations and the disability community around the globe. Researchers usually use colonial methodology that does not encompass the values of these communities or have their well-being in mind. This paper examines the history of colonization and how it has infiltrated science and inhibits self-determination of Indigenous peoples. Indigenous communities need to have the means and power for self-determination. For individuals with disabilities, this includes rights to services and programs that give the respect and person-centered care they deserve to make informed decisions about their lives. Moreover, there is a recognized need for culturally appropriate services that empower American Indian and Alaska Native (AI/AN) people with disabilities to lead independent lives in their own communities—urban or rural. AI/AN cultures may view disabilities differently than those in the mainstream U.S. Barriers and challenges for AI/AN individuals with intellectual and developmental disabilities (IDD) and AI/AN families of individuals with IDD in access to services include inadequate funding, personnel shortages, housing shortages, lack of coordination among agencies, lack of consultation with tribes, and problems identifying persons eligible for services. AI/AN-specific programs that have begun to bridge the gap in access to and development of culturally competent services such as Oyáte Circle and development of collegiate courses focused on AI/AN disabilities issues. There remains a need for partnership with AI/AN tribes for disability services and incorporation of AI/AN people with disabilities as equitable partners in program development and implementation. To reach a full decolonization of IDD health care and fully embrace diversity, equity, and inclusion (DEI) principles, individuals in these communities need to be viewed as experts in their journey of resilience.

Babalwa Tshaka, Surona Visagie, Lieketseng Y. Ned

Background: Access to primary health care is a fundamental right for all. However, persons with disabilities are experiencing difficulties when accessing healthcare because of various environmental and personal barriers which may lead to nonuse of such services. Objectives: This study aimed to identify the challenges leading to non-use of healthcare services among persons with mobility impairments in Cofimvaba. Method: A descriptive qualitative design using snowball sampling was implemented. Semistructured interviews were conducted in isiXhosa with five participants who stopped accessing healthcare, using a self-developed interview guide. Inductive thematic analysis was used to develop codes and themes from the data. Results: Study findings revealed major challenges experienced by persons with mobility impairments in accessing healthcare. These included inaccessible roads, geographic inaccessibility, financial accessibility and indirect cost of care, having little or not many health problems, physical infrastructure difficulties within facilities, and attitudinal barriers. Conclusion: The findings indicated that persons with disabilities are experiencing a combination of structural and environmental challenges which make them stop accessing healthcare. Contribution: The article shares insights on access challenges that influence non-use of the often-needed healthcare services within the context of rural areas.

Ndakaitei Manase

Background: Access to assistive technology for disabilities is limited in low-resource settings. Therefore, existing research focuses on accessibility challenges. This article focuses on how students with learning disabilities manage learning in the absence of assistive technology, a subject that receives less scholarly attention. Objectives: This article aims to provide insights on how students with learning disabilities manage learning in the face of limited access to assistive technology. It explores conversion factors that influence access to assistive technology. Method: This qualitative study used semistructured interviews to collect data from students with learning disabilities and respective university staff members who were recruited using convenience and snowballing techniques. Data were analysed thematically and supported by thick descriptions of experiences. Results: This study established that students have limited access to assistive technology, and they manage learning through self-devised means that are more socially than technologically or scientifically inspired such as self-affirmation, animal therapy, family support and prayer. Conversion factors, which affect ability by either enabling or constraining access to assistive technology, were identified at personal and institutional or environmental levels. Conclusion: The article concludes that even though students with learning disabilities devise unconventional assistive ways to manage learning, failure to access assistive technology is a capability deprivation that promotes inequalities. Contribution: This article provides insights that shift perspectives that students with disabilities are passive recipients of support; rather, they can be active agents who innovate nontechnological ways to manage learning in the absence of assistive technology.

Simon Wundling

If you want to fill $n \in \mathbb{N}$ seats in succession with $n$ people and the rule that each person chooses one of the seats with the maximum distance to an occupied seat, then you can ask yourself how many possibilities there are for this. In this paper, based on initially mentioned ideas, a formula for the number of these possibilities will be found. In addition, a lower and upper bound for this formula will be given. Finally, formulas for the OEIS sequences A166079, A095236, A095240 and A095912 and an extension of the initial problem are derived.

Zhangchi Lu, Mertcan Cokbas, Prakash Ishwar et al.

Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fisheye lens, whereas the other method uses a neural network to predict the 3D-world distance from people-locations in a fisheye image. To evaluate our algorithms, we collected a first-of-its-kind dataset using single fisheye camera, that comprises a wide range of distances between people (1-58 ft) and will be made publicly available. The algorithms achieve 1-2 ft distance error and over 95% accuracy in detecting social-distance violations.

Amanda E. Gibberd, Ntombizivumile Hankwebe

Transport is a known national barrier for people with disabilities in South Africa. It is similarly identified as a barrier in learnerships and economic opportunity programmes. This article discusses the extent to which transport is a barrier during learnerships for students with disabilities. The Department of Transport administered an online evaluation questionnaire to a random sample of students with disabilities. Results were coded in terms of ‘barriers to access’ and ‘barriers to participation’. The data were organised into themes. The collated evidence is discussed in this article. The findings demonstrated that transport barriers were present in different modes of transport and different parts of the travel chain. However, the findings also demonstrated the negative impact of transport on the learnership experience and economic opportunities. The findings indicated that inaccessible transport is an integral cause of learnership incompletion for students with disabilities, where the universal accessibility of both transport and the built environment are a prerequisite need. Most students with disabilities reported that transport was not a barrier to learnership participation or that problems with transport could be resolved. Nevertheless, it was one of the identified barriers that negatively affected learnership participation experiences. It was a significant barrier to learnership completion for students with the most severe experience of disability. The sample consisted of only 32 students and a high number of unspecified responses. Evidence from other studies indicates that transport for all persons with disabilities remains a barrier warranting further examination, because public transport has remained inaccessible for over 23 years. Further research is required to verify this study and to investigate learnership cost–benefit for all students.

Jennifer G.R. Kromberg, Robyn Kerr

Albinism is an inherited condition associated with significant depigmentation of the skin, hair and eyes. It occurs in every population with varying frequency, and narratives of people with albinism have been recorded since 200 BC. In southern Africa albinism is common, about 1 in 4000 people are affected, but it remains a poorly understood condition surrounded by myths and superstition. This article provides a historical background on oculocutaneous albinism (OCA) in southern Africa and presents relevant information from the literature regarding epidemiology, genetics and genetic counselling, health, psychosocial and cultural issues, and medical care. There are several recessively inherited types of OCA and a mutation, responsible for about 80% of South African variants, has been identified in OCA type 2. The physical characteristics associated with albinism, that is, sun-sensitive skin and low vision, can be managed. However, people with OCA in Africa also experience psychosocial issues, such as discrimination, because of the various superstitious beliefs and attitudes held in the community. Management should include medical care for health problems, appropriate adjustment of the schooling context and genetic counseling. In addition, widespread public awareness programmes are required to increase the knowledge of the genetic causes of OCA and of the nature of genetic counselling, to address the negative attitudes in the community, to reduce the marginalisation and stigmatization of people with albinism and to improve their quality of life.