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The goal of the Convention on the Rights of Persons with Disabilities (CRPD) is to secure the rights of persons with disabilities as full and equal members of society. Achieving these objectives requires appropriate and high-quality data for identifying disability gaps and areas of exclusion. Such data can be then used to develop policies and programmes to address that exclusion, and then to monitor and evaluate their effectiveness. However, researchers and policy makers face a number of challenges when collecting this data. This chapter reviews the various approaches to collecting data on disability, identifying potential sources of relevant data, including through surveys and repurposing of data collated for administrative means. It discusses at length the legal and ethical issues involved in their collection and use for alternative purposes, providing a comprehensive exploration of issues relating to participation, privacy, big data and rights of access. It concludes by suggesting ways forward for creating disability data systems that can fully support efforts to achieve a fully inclusive society.

Rural development programs are widely used policy instruments mitigating rural-urban economic disparities. Yet, little research has examined their effect on rural labor. This study fills this knowledge gap by quantifying the causal impact of such programs on the labor allocation of farm households in Taiwan.

A theoretical framework based on the agricultural household model is constructed to guide the empirical specification. A unique dataset compiles administrative data on the program’s subsidies with farm household surveys across seven years. To cope with endogeneity bias, an instrumental variables model is applied. The eligibility rule for a township to participate in the program is used as the instrument.

We find that the program increases the labor supply of farm household members. These effects are more pronounced for off-farm work, particularly non-heads of farm households. The program’s subsidies supporting culture and promotion-related activities have larger effects. Finally, females benefited more from the program.

We focus on farm households since this group is the target of place-based rural development programs. In addition, we identify the causal impact of place-based development programs on rural labor. Finally, this study is relevant to the literature on intra-household models by demonstrating that place-based rural development programs can affect the labor supply of farm household members.

Effective use of data is critically important for the provision of health services. A large proportion of employees in health organisations work in non-clinical roles and play a major part in organisational information flows. However, their practice, data-related capabilities and learning needs have been rarely studied. The purpose of this paper is to investigate issues of capabilities and learning needs related to employees' interactions with data in non-clinical work roles.

The study used a mixed-method approach. Qualitative methods were used to explore issues, and survey was administered to gather additional data.

Data use and related capabilities at the workplace are highly contextual. A range of general, core and data-specific capabilities, underpinned by transferable skills and personal traits, enable successful interactions with data. Continuous learning is needed in most areas related to data use.

The study was conducted in a large public-health organisation in Australia, which is not representative of unique organisations elsewhere. The study has implications for the provision of health services, workplace learning and education.

Findings have implications for organisational decisions related to data-use and workplace learning, and for formal education and lifelong learning.

The study contributes to closing a research gap in understanding interactions with data, capabilities and learning needs of employees in non-clinical work roles. Capabilities continuum presented in this paper can be used to inform education, training and service provision. The workplace-based results contribute to theoretical considerations of capabilities required for work in technology-rich environments.

Looked after children (LAC) are criminalised at five times the rate of children in the general population. Children in contact with both child welfare and child justice systems have higher rates of neurodisability and substance use problems, and LAC in general have high rates of school exclusion, homelessness and unemployment. This study aims to understand whether these factors persist in LAC who are in prison as adults.

Administrative data collected by the Do-IT profiler screening tool in a prison in Wales, UK, were analysed to compare sentenced prisoners who were LAC (n = 631) to sentenced prisoners who were not LAC (n = 2,201). The sample comprised all prisoners who were screened on entry to prison in a two-year period.

Prisoners who were LAC scored more poorly on a functional screener for neurodisability (effect size = 0.24), and on four self-report measures capturing traits of dyslexia (0.22), attention-deficit hyperactivity disorder (0.40), autism spectrum disorders (0.34) and developmental co-ordination disorder (0.33). Prisoners who were LAC were more likely to have been to a pupil referral unit (0.24), have substance use problems (0.16), be homeless or marginally housed (0.18) and be unemployed or unable to work due to disability (0.13).

This study uniquely contributes to our understanding of prisoners who were LAC as a target group for intervention and support with re-integration into the community upon release. LAC in prison as adults may require additional interventions to help with employment, housing and substance use. Education programmes in prison should screen for neurodisability, to develop strategies to support engagement.

This study aims to review the current literature on the positive and negative effects of digitalisation in preventing corruption. It analyses existing research patterns and provides recommendations for future studies.

This paper employed bibliometric analysis and systematic review to scrutinise 190 papers from the Web of Science database from 2000 to 2023. Biblioshiny on R Studio was used for advanced bibliometric analysis to determine publication dynamics, influential journals, publications and impactful authors and a three-field plot to analyse relationships among countries, keywords and journals.

This study provides a bibliometric analysis of the past and actual developments in the field related to the effects of digitalisation on corruption. Based on the systematic literature review on a sample of the 50 most influential articles, this study identified background theories employed, the primary research methodologies adopted and valuable insights into both the positive and negative aspects of the impact of digitalisation on corruption.

This study provides an extended overview of the effects of digitalisation on corruption and advances new avenues for further research related to this field. The white and dark sides of the effects of digitalisation on corruption are highlighted. Furthermore, the study identifies the need for further research in this field to gain a more in-depth understanding of the nexus between digitalisation and corruption.

This study investigates the pathways for adopting IoTs and BDA technologies to improve healthcare management.

The study relied on 445 healthcare professionals' perspectives to explore different causal pathways to IoTs and BDA adoption and usage for daily healthcare management. The Fussy-set Qualitative Comparative Analysis was adopted to explore the underlying pathways for healthcare management.

The empirical analysis revealed six different configural paths influencing the acceptance and use of IoTs and BDA for healthcare improvement. Two key user topologies from the six configural paths, digital literacy and ease of use and social influence and behavioural intentions, mostly affect the paths for using digital health technologies by healthcare physicians.

Despite this study's novel contributions, limitations include the fsQCA methodology, perceptual data and the context of the study. The fsQCA methodology is still evolving with different interpretations, although it reveals new insights and as such further studies are required to explain the configural paths of social phenomena. Additionally, future research should consider other constructs beyond the UTAUT and digital literacy to illustrate configural paths to healthcare technology acceptance and usage. Again, the views of healthcare professionals are perceptual data. Hence future research on operational data will support significant contributions towards pathways to accept and use emerging technologies for healthcare improvement. Lastly, this study is from a developing country perspective where emerging digital healthcare technology is still emerging to support healthcare management. Hence, more investigation from other cross-country analyses of configural paths for digital technology deployment in healthcare will enhance the conversation with IoTs and BDA for healthcare management.

Holistically, the acceptance and use of healthcare technologies and platforms is not solely on their capabilities, but a combination of distinct factors driven by users' perspectives. This offers healthcare administrators and institutions to essentially reflect on the distinct combinations of conditions favourable to health professionals who can use IoTs and BDA for healthcare improvement.

This study is among the few scholarly works to empirically investigate the configural paths to support healthcare improvement with emerging technologies. Using fsQCA is a unique contribution to existing information system literature for configural paths for healthcare improvement with emerging digital technologies.

Continuity of care and access to primary care have been identified as important contributors to improved health outcomes and reduced reincarceration among people who are justice-involved. While the disproportionate burden of health concerns among incarcerated populations is well documented, less is known about their health service utilization, limiting the potential for effective improvements to current policy and practice. This study aims to examine health status and health care utilization among men recently released from a superjail in a large metropolitan area to better understand patterns of use, risk factors and facilitators.

Participants included adult men (n = 106) matched to a general population group (n = 530) in Ontario, Canada, linked to medical records (88.5% linkage) to examine baseline health status and health utilization three-months post-release. The authors compared differences between the groups in baseline health conditions and estimated the risk of emergency department, primary care, inpatient hospitalization and specialist ambulatory care visits.

Superjail participants had a significantly higher prevalence of respiratory conditions, mental illness, substance use and injuries. Substance use was a significant risk factor for all types of visits and emergency department visits were over three times higher among superjail participants.

This empirical case is illustrative of an emerging phenomenon in some regions of the world where emergency departments serve as de facto “walk-in clinics” for those with criminal justice involvement. Strategic approaches to health services are required to meet the complex social and health needs and disparities in access to care experienced by men released from custody.

This study proposes an integrated Machine Learning and simulated framework for a personalized learning system. This framework aims to improve the integrity of the provided tasks, adapt to each student individually and ultimately enhance students' academic performance.

This methodology comprises two components. (1) A simulation-based system that utilizes reinforcement algorithms to assign additional questions to students who do not reach pass grade thresholds. (2) A Machine Learning system that uses the data from the system to identify the drivers of passing or failing and predict the likelihood of each student passing or failing based on their engagement with the simulated system.

The results of this study offer preliminary evidence of the effectiveness of the proposed simulation system and indicate that such a system has the potential to foster improvements in learning outcomes.

As with all empirical studies, this research has limitations. A simulation study is an abstraction of reality and may not be completely accurate. Student performance in real-world environments may be higher than estimated in this simulation, reducing the required teacher support.

The developed personalized learning (PL) system demonstrates a strong foundation for improving students' performance, particularly within a blended learning context. The findings indicate that simulated performance using the system exhibited improvement when individual students experienced higher learning benefits tailored to their needs.

The research offers evidence of the effectiveness of personalized learning systems and highlights their capacity to drive improvements in education. The proposed system holds the potential to enhance learning outcomes by tailoring tasks to meet the unique needs of each student.

This study contributes to the growing literature on personalized learning, emphasizing the importance of leveraging machine learning in educational technologies to enable precise predictions of student performance.

We enquire whether short-time work (STW) avoids firings as intended by policymakers and is associated with unintended side effects by subsidising some establishments and locking in some employees. Additionally, where it was feasible, establishments used working from home (WFH) to continue working without risking an increase in COVID-19 infections and allowing employed parents to care for children attending closed schools.

Using 21 waves of German high-frequency establishment panel data collected during the COVID-19 crisis, we investigate how STW and WFH are associated with hirings, firings, resignations and excess labour turnover (or churning).

Our results show the important influences of STW and working from home on employment dynamics during the pandemic. By means of STW, establishments are able to avoid an increase in involuntary layoffs and hiring decreases significantly. In contrast, WFH is associated with a rise in resignations, as can be expected from a theoretical perspective.

While most of the literature on STW and WFH is unrelated and remains descriptive, we consider them in conjunction and conduct panel data analyses. We apply data and methods that allow for the dynamic pattern of STW and working from home during the pandemic. Furthermore, our data include relevant establishment-level variables, such as the existence of a works council, employee qualifications, establishment size, the degree to which the establishment was affected by the COVID-19 crisis, industry affiliation and a wave indicator for the period the survey was conducted.

This survey-based study aims to explore the research data dissemination and requesting practices of Arab researchers. It investigates the reasons, types, methods, barriers and motivations associated with data sharing and requesting in the Arab research community.

A cross-sectional survey was conducted with 205 Arab researchers representing various disciplines and career stages. Descriptive statistics were used for data analysis.

The study found that 91.2% of Arab researchers share data, while 56.6% access data from others. Reasons for sharing include promoting transparency and collaboration while requesting data is driven by the need to validate findings and explore new research questions. Processed/analysed data and survey/questionnaire data are the most commonly shared and requested types.

This study contributes to the literature by examining data sharing and requesting practices in the Arab research community. It provides original insights into the motivations, barriers and data types shared and requested by Arab researchers. This can inform future research and initiatives to promote regional data sharing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0283