Background: In Zimbabwe, disability is defined and explained from a cultural, African indigenous religion and spirituality. These perspectives are sources of exclusion of persons with disabilities (PWD) from important social positions, economic empowerment, rites of passage and ceremonies.
Objectives: This study explores the politics of disability and belonging in Zimbabwe.
Method: Using Marco Antonsich’s conceptualisation of place-belongingness and the politics of belonging, data are collected through qualitative desktop methodology, which explores how people with disabilities in Zimbabwe make emotional connections with communal spaces while being excluded from them.
Results: The study findings show the struggles for PWD to belong to religious or spiritual practices and cultural and economic spaces in Zimbabwe. The conventional concept of Ubuntu, which calls for interconnection of humans, provides hope to include PWD as part of the community. Disability in Zimbabwe is not one thing; the experiences of PWD are influenced by overlapping identities (such as gender, class and religion). The legacy of colonialism is visible in the spatial exclusion of the PWD, especially in the urban informal settlements, which sustain the pre-existing trends of exclusion.
Conclusion: The study concludes that belonging and belongingness of PWD should be within the grassroots movements and Ubuntu ethics, which encourage humaneness.
Contribution: The study contributes to policy changes informed by decolonised disability justice, participatory governance and the integration of Ubuntu principles to create a more inclusive citizenship and a stronger communal belonging for people with disabilities in Zimbabwe.
Vocational rehabilitation. Employment of people with disabilities, Communities. Classes. Races
The US economy is transitioning away from fossil fuels toward sources of green energy. California policymakers have adopted the goal of carbon neutrality by 2045 or earlier. Within California, Kern County accounts for over 70 percent of oil produced within the state. To understand how the transition may affect opportunities in Kern, we propose a structural vector autoregressive model the jointly explains the crude-oil market and the evolution of employment in Kern. We use monthly data from the Quarterly Census of Employment and Wages to measure employment. While industries directly involved in the extraction of fossil fuels employ less than 2 percent of workers, the oil market is responsible for 11 percent of the variation in employment growth. Employment in Kern would be currently 6.4 percent lower absent the influence of the global oil market. We explain these large effects using a theoretical framework of production that relies on a network of input-output linkages. The findings may be useful to policymakers designing place-based policy aimed at helping vulnerable oil-dependent regions.
Richard J Cowan, Phillip Rumrill, Muharrem Koc
et al.
Background People with learning disabilities (LDs) are a large and growing segment of the American and global disability communities, but they tend to underutilize vocational rehabilitation (VR) services. Objective This article presents a contextual career development framework, the Illinois Work and Well-Being Model (IW 2 M), as an intervention schematic that can be applied to facilitate positive VR outcomes for people with LDs. Methods The framework encompasses the interaction of contextual and career development domains to improve participation in the areas of work, society, community, and home. Results A case study is used to illustrate how the framework can be used in VR assessment, case planning, and service delivery. Implications A discussion of implications regarding VR practice and research is offered, with the overall goal of reinforcing career development as the foundation of VR services for people with LD and other disabilities. Conclusions VR professionals can use the health, function, and career development framework to provide responsive, goal-oriented, and customer-directed services that lead to a wide range of positive outcomes for VR participants with LD.
Lumka Magidigidi-Mathiso, Jose Frantz, Gerard C. Filies
Background: Developmental disabilities (DDs) involve impairments affecting children’s abilities, impacting development and necessitating specialised care. Many caregivers face challenges caring for these children, lacking access to supportive healthcare interventions. Addressing this issue aligns with United Nations (UN) goals for ensuring access to quality services for children with disabilities and their caregivers.
Objectives: This study aimed to systematically review and synthesise evidence on healthcare interventions enhancing caregiver capabilities for children with DDs, identifying intervention types, components and effectiveness.
Method: Our systematic review analysed peer-reviewed English-language studies from 2014 to 2024, focusing on interventions for caregivers of children with DDs. The review investigated healthcare interventions designed to enhance caregiver capabilities across diverse cultural contexts, examining international research to understand strategies supporting caregivers of children with DDs.
Results: We found significant improvements in caregiver well-being through five interventions. Parent education reduces stress and improves parenting. Peer support decreased isolation while counselling enhanced family functioning. Condition-specific interventions increased intervention adherence among minorities. Combined interventions showed strong positive effects, especially when tailored. Comprehensive programmes greatly improved caregiver quality of life. Further research is needed for underserved communities and culturally adaptive interventions.
Conclusion: Our review indicates potential positive parental impacts with limited evidence. Small samples warrant future research using larger studies, emphasising rigorous methods, cultural adaptation and diverse community representation.
Contribution: Our review identifies promising intervention types and highlights the need for further research to optimise caregiver support and promote access to quality services.
Vocational rehabilitation. Employment of people with disabilities, Communities. Classes. Races
Background: Urdu has historically lacked validated speech audiometry materials, limiting clinicians to improvised and unreliable tests. Recently developed Urdu spondee and monosyllabic word lists have been validated in normal-hearing listeners. This study evaluated their clinical utility in hearing-impaired adults.
Methods: Forty Urdu-speaking adults aged 18–60 years including conductive loss (n=10), mild sensorineural hearing loss (SNHL; n=10), moderate SNHL (n=10), and moderately severe SNHL (n=10) and with bilateral hearing loss were recruited in the study. Pure-tone audiometry was used as an established threshold. Speech Reception Thresholds (SRT) were measured using the Urdu spondee list. While Word Recognition Scores (WRS) were assessed using validated monosyllabic lists at four intensity levels (10 dB above SRT to 5 dB below uncomfortable loudness level).
Results: pure-tone averages as SRTs raised systematically with severity and closely matched, the mild SNHL 34.5±3.7 dB (PTA 35.6±4.8), moderate SNHL 49.5±3.7 dB (PTA 52.5±3.8), moderately severe SNHL 62.1±2.5 dB (PTA 63.6±4.2), and conductive HL 42.0±5.4 dB (PTA 45.0±5.0). Word recognition improved with intensity across all groups. Conductive HL achieved the highest scores up to 100%, mild SNHL up to 89%, moderate SNHL up to 78%, and moderately severe SNHL up to 69%. Performance-intensity curves showed up a plateau at higher levels with no rollover.
Conclusions: The Urdu spondee and monosyllabic lists yielded results consistent with audiological norms. SRTs agreed closely with audiometric thresholds, and WRS patterns reflected hearing loss type and severity. These findings validate the new Urdu materials as reliable clinical tools for Urdu-speaking patients with hearing impairment.
Vocational rehabilitation. Employment of people with disabilities
Throughout the COVID-19 pandemic, people with disabilities were particularly inactive and online programs offered new opportunities for their engagement in leisure time physical activity (LTPA). However, research regarding how virtual adaptive LTPA programs were developed and delivered is limited. This case study examined the staff perceptions of developing and delivering virtual adaptive LTPA for people with disabilities to better understand the barriers and facilitators organizations experienced during its implementation. Five semi-structured interviews conducted with staff of a community-based organization specializing in adaptive LTPA explored the process of transitioning to a virtual format, challenges and facilitators, and lessons learned. Interviews were analyzed using inductive content analysis and organized around three themes. <i>Systematic development of virtual programs</i> suggested that supporting the organization, staff and members facilitates virtual adaptive LTPA. <i>Creativity and resourcefulness in delivering programs</i> highlighted the importance of flexible management and using staff skills. <i>Effects of transitioning to virtual programming</i> emphasized positive impacts of virtual LTPA such as connectedness, health benefits, and expanded organizational reach. The results of this study emphasize the benefits of virtual adaptive LTPA and highlight key management factors that organizations can use to provide virtual opportunities for adaptive LTPA for people with disabilities.
Vocational rehabilitation. Employment of people with disabilities
Michał Ćwiąkała, Dariusz Baran, Gabriela Wojak
et al.
This paper examines the role of employment flexibility in enhancing the competitiveness of firms using temporary staffing services, with empirical evidence from Poland. The study focuses on how flexible employment arrangements influence operational efficiency, cost reduction, workforce scalability, market responsiveness, and client satisfaction. A quantitative survey was conducted among managers and owners of Polish enterprises that cooperate with temporary staffing agencies, using purposeful sampling to capture informed managerial perspectives. The findings show that employment flexibility significantly reduces downtime, accelerates onboarding processes, and lowers personnel and recruitment costs. Flexible staffing enables rapid workforce scaling during demand fluctuations and facilitates access to specialized skills without long-term commitments. The results also indicate that employment flexibility enhances organizational responsiveness, improves profitability in short-term projects, and strengthens resilience to seasonal and market volatility. Additionally, flexibility is identified as a key determinant of client satisfaction and loyalty toward staffing service providers. The study demonstrates that employment flexibility is not merely a cost-control mechanism but a strategic human resource capability that supports competitiveness, operational adaptability, and sustainable performance in dynamic labor markets.
Therapist-in-the-loop robotic rehabilitation has shown great promise in enhancing rehabilitation outcomes by integrating the strengths of therapists and robotic systems. However, its broader adoption remains limited due to insufficient safe interaction and limited adaptation capability. This article proposes a novel telerobotics-mediated framework that enables therapists to intuitively and safely deliver assist-as-needed~(AAN) therapy based on two primary contributions. First, our framework encodes the therapist-informed corrective force into via-points in a latent space, allowing the therapist to provide only minimal assistance while encouraging patient maintaining own motion preferences. Second, a shape-adaptive ANN rehabilitation policy is learned to partially and progressively deform the reference trajectory for movement therapy based on encoded patient motion preferences and therapist-informed via-points. The effectiveness of the proposed shape-adaptive AAN strategy was validated on a telerobotic rehabilitation system using two representative tasks. The results demonstrate its practicality for remote AAN therapy and its superiority over two state-of-the-art methods in reducing corrective force and improving movement smoothness.
Julian Leichert, Monique Koke, Britta Wrede
et al.
In this work, we evaluate the feasibility of socially assistive virtual agent-based cognitive training for people with intellectual disabilities (ID) in a sheltered workshop. The Robo- Camp system, originally developed for children with Attention Deficit Hyperactivity Disorder (ADHD), is adapted based on the results of a pilot study in which we identified barriers and collected feedback from workshop staff. In a subsequent study, we investigate the aspects of usability, technical reliability, attention training capabilities and novelty effect in the feasibility of integrating the RoboCamp system.
Srikant Panda, Amit Agarwal, Hitesh Laxmichand Patel
Large Language Models (LLMs) are increasingly deployed across diverse domains but often exhibit disparities in how they handle real-life queries. To systematically investigate these effects within various disability contexts, we introduce \textbf{AccessEval (Accessibility Evaluation)}, a benchmark evaluating 21 closed- and open-source LLMs across 6 real-world domains and 9 disability types using paired Neutral and Disability-Aware Queries. We evaluated model outputs with metrics for sentiment, social perception, and factual accuracy. Our analysis reveals that responses to disability-aware queries tend to have a more negative tone, increased stereotyping, and higher factual error compared to neutral queries. These effects show notable variation by domain and disability type, with disabilities affecting hearing, speech, and mobility disproportionately impacted. These disparities reflect persistent forms of ableism embedded in model behavior. By examining model performance in real-world decision-making contexts, we better illuminate how such biases can translate into tangible harms for disabled users. This framing helps bridges the gap between technical evaluation and user impact, reinforcing importance of bias mitigation in day-to-day applications. Our dataset is publicly available at: https://huggingface.co/datasets/Srikant86/AccessEval
This paper assesses the global employment and trade effects of renewed tariff escalation following the reintroduction of the United States' America First strategy in 2025. Using a multiregional input-output (MRIO) framework integrated with a trade model, the analysis captures endogenous adjustments in bilateral trade shares and final demand in response to changes in prices and competitiveness. Three scenarios are simulated to reflect alternative configurations of trade policy: existing tariffs without retaliation, updated tariffs including retaliatory measures, and a potential scenario characterized by de-escalation of the trade conflict. The results indicate that tariff increases generate widespread employment and export losses, with cumulative global job declines exceeding 23 million in the most adverse scenario. Informal and low-skilled workers bear the largest burden, accounting for more than 80 percent of total employment losses, while high-income and upper middle-income countries experience significant contractions in export volumes.
Kathryn K. Yamamoto, Lanelle Yamane, Lisa M. C. Fukumitsu
This article describes the current status of vocational rehabilitation (VR) services in the State ofHawai’i as seen through the eyes ofVR professionals. We provide a general picture of the State ofHawai’i Division of Vocational Rehabilitation (DVR) Program and focus a large part of our discussion on recent legislation, specifically, the Workforce Innovation and Opportunity Act (WIOA) and its impact on the provision of services to persons with disabilities. In the final section of this article, we discuss the state ofVR in the private sector and share some of the challenges encountered by VR providers in the State.
Fengyi Gao, Xingyu Zhang, Sonish Sivarajkumar
et al.
In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients' rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. We collected data for 265 stroke patients from the University of Pittsburgh Medical Center. We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models-logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)-to predict outcomes in functional ability. Statistical analyses revealed significant associations between functional improvements and specific exercises. The RF model achieved the best performance in predicting functional outcomes. In this study, we identified three rehabilitation exercises that significantly contributed to patient post-stroke functional ability improvement in the first two months. Additionally, the successful application of a machine learning model to predict patient-specific functional outcomes underscores the potential for precision rehabilitation.
Md Abdul Baset Sarker, Juan Pablo Sola-thomas, Masudul H. Imtiaz
Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.
F. Xavier Gaya-Morey, Silvia Ramis, Jose M. Buades-Rubio
et al.
Facial expression recognition has gained significance as a means of imparting social robots with the capacity to discern the emotional states of users. The use of social robotics includes a variety of settings, including homes, nursing homes or daycare centers, serving to a wide range of users. Remarkable performance has been achieved by deep learning approaches, however, its direct use for recognizing facial expressions in individuals with intellectual disabilities has not been yet studied in the literature, to the best of our knowledge. To address this objective, we train a set of 12 convolutional neural networks in different approaches, including an ensemble of datasets without individuals with intellectual disabilities and a dataset featuring such individuals. Our examination of the outcomes, both the performance and the important image regions for the models, reveals significant distinctions in facial expressions between individuals with and without intellectual disabilities, as well as among individuals with intellectual disabilities. Remarkably, our findings show the need of facial expression recognition within this population through tailored user-specific training methodologies, which enable the models to effectively address the unique expressions of each user.
Sao Mai Nguyen, Maxime Devanne, Olivier Remy-Neris
et al.
While automatic monitoring and coaching of exercises are showing encouraging results in non-medical applications, they still have limitations such as errors and limited use contexts. To allow the development and assessment of physical rehabilitation by an intelligent tutoring system, we identify in this article four challenges to address and propose a medical dataset of clinical patients carrying out low back-pain rehabilitation exercises. The dataset includes 3D Kinect skeleton positions and orientations, RGB videos, 2D skeleton data, and medical annotations to assess the correctness, and error classification and localisation of body part and timespan. Along this dataset, we perform a complete research path, from data collection to processing, and finally a small benchmark. We evaluated on the dataset two baseline movement recognition algorithms, pertaining to two different approaches: the probabilistic approach with a Gaussian Mixture Model (GMM), and the deep learning approach with a Long-Short Term Memory (LSTM). This dataset is valuable because it includes rehabilitation relevant motions in a clinical setting with patients in their rehabilitation program, using a cost-effective, portable, and convenient sensor, and because it shows the potential for improvement on these challenges.
Policy Points Employment is a key social determinant of health and well-being for the estimated 5.4 million autistic adults in the United States-just as it is for citizens without disabilities. Evaluation and monitoring of publicly funded employment services is paramount given the dramatic increases in adults with autism who need job supports. Vocational Rehabilitation agencies appeared to be absorbing short-term employment needs of autistic people, but Medicaid was severely lacking-and losing ground-in serving those who need longer-term employment services. Across both Vocational Rehabilitation and Medicaid, we estimated that only 1.1% of working-age autistic adults who potentially need employment services are actually receiving them-leaving an estimated 1.98 million autistic individuals without the employment services that are associated with achievement of well-being. CONTEXT Employment is a key social determinant of health. As such, high rates of unemployment, underemployment, and poverty across the rapidly growing autistic population are concerning. A web of publicly funded services exists to support the employment, and associated health and well-being, of United States citizens with autism and other intellectual and developmental disabilities, namely through Vocational Rehabilitation (VR) and Medicaid home- and community-based services (HCBS) waivers. Given an absence of overarching surveillance of employment services, this study aimed to characterize the distribution of autistic service users across Medicaid versus VR, understand the types of employment services utilized within these programs and expenditures, and assess overall capacity to provide employment services as needs continue to increase. METHODS This study examined the distribution of employment services among autistic people compared with those with intellectual disability using 2008-2016 data from the Centers for Medicare & Medicaid Services and the Rehabilitation Services Administration. Estimated need for employment services among autistic individuals was compared with capacity derived from VR service counts and a review of HCBS waivers. FINDINGS The number of autistic people served through VR tripled during the study years, whereas those served through Medicaid only increased slightly. VR spending increased by 384% over the study years, whereas Medicaid costs decreased by 29%. Across VR and Medicaid, we estimated that only 1.1% of working-age autistic adults who needed employment services received them. CONCLUSIONS Although VR appeared to be absorbing short-term employment needs of autistic individuals, Medicaid was severely lacking-and losing ground-in serving those who needed longer-term employment services. VR far outpaced Medicaid in both the number of autistic people served and total expenditures across the study years. However, an estimated 1.98 million autistic adults did not receive employment services that could be critical to improving their health and well-being.
This article summarizes data collected from key informants in Iowa, Maryland, and Oklahoma regarding efforts to support integrated employment for people with intellectual and developmental disabilities (IDD). We highlight features that contribute to the effectiveness of collaborative structures that have resulted in each state's success in achieving integrated employment outcomes for individuals with IDD across three state systems: IDD, vocational rehabilitation, and education. We present these features using the seven elements of the High-Performing States Employment Model. These elements have been found to be important in achieving higher rates of competitive integrated employment outcomes for people with IDD.
BACKGROUND: To date, the Individual Placement and Support (IPS) model is the only vocational intervention that has been rigorously studied and shown to be effective with Veterans with spinal cord injury (SCI). Customized Employment (CE) is an innovative vocational intervention with promising results among people with disabilities which has yet to be tested in persons with SCI. OBJECTIVE: To determine whether a Customized Employment (CE) intervention adapted for SCI rehabilitation is more effective than the standard care (IPS) for helping Veterans with SCI obtain and maintain employment. METHODS: A 4-year, 2-site randomized clinical trial (RCT) with concurrent mixed methods using an intent-to-treat (ITT) approach. The primary outcome is competitive integrated employment as defined by the Work Innovation and Opportunity Act. Secondary outcomes are employment indicators, quality of life (QOL), and participation. RESULTS: This is a methods paper so there are no results to present at this time. CONCLUSION: The proportion of Veterans who attain employment will be greater for the CE group than the IPS group and they will outperform the IPS group on other employment-related metrics (e.g., higher job satisfaction, wages, and retention). Employed Veterans will demonstrate significant improvements in self-sufficiency, QOL, and participation. Qualitative data obtained from interviews will assist with adaptation strategies and will identify barriers to implementing CE.