The exchange and use of health information can help healthcare professionals and policymakers make informed decisions on ways of improving patient and population health. Many low- and middle-income countries (LMICs) have however failed to embrace the approaches and technologies to facilitate health information exchange (HIE). We sought to understand the barriers and facilitators to the implementation and adoption of HIE in LMICs. Two reviewers independently searched 11 academic databases for published and on-going qualitative, quantitative and mixed-method studies and searched for unpublished work through the Google search engine. The searches covered the period from January 1990 to July 2014 and were not restricted by language. Eligible studies were independently, critically appraised and then thematically analysed. The searches yielded 5461 citations after de-duplication of results. Of these, 56 articles, three conference abstracts and four technical reports met the inclusion criteria. The lack of importance given to data in decision making, corruption and insecurity, lack of training and poor infrastructure were considered to be major challenges to implementing HIE, but strong leadership and clear policy direction coupled with the financial support to acquire essential technology, improve the communication network, and provide training for staff all helped to promote implementation. The body of work also highlighted how implementers of HIE needed to take into account local needs to ensure that stakeholders saw HIE as relevant and advantageous. HIE interventions implemented through leapfrog technologies such as telehealth/telemedicine and mHealth in Brazil, Kenya, and South Africa, provided successful examples of exchanging health information in LMICs despite limited resources and capability. It is important that implementation of HIE is aligned with national priorities and local needs.

Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis.

Social entrepreneurship (SE) is an all-encompassing concept in comparison to a typical non-government organization (NGO). It is a topic that has captured the interest of academics investigating nonprofit, charitable, and nongovernmental organizations. Despite the interest, few studies have examined the overlap and convergence of entrepreneurship and non-governmental organizations (NGOs), in congruence with the new phase of globalization. The study gathered and evaluated 73 peer-reviewed papers using a systematic literature review methodology, mainly from Web of Science but also from Scopus, JSTOR, and Science Direct, and supplemented by a search of existing databases and bibliographies. Based on the findings, 71 percent of studies suggest that organizations must reconsider the concept of social work, which has evolved rapidly, aided by globalization. The concept has changed from the NGO model to a more sustainable one, such as that proposed by SE. However, it is difficult to draw broad generalizations regarding the convergence of context-dependent complex variables such as SE, NGOs, and globalization. The results of the study will significantly contribute to a better understanding of the convergence of SE and NGOs, as well as the recognition that many aspects of NGOs, SE, and post-COVID globalization remain unexamined.

This interview features a prominent disability rights advocate who has been instrumental in shaping the discourse on inclusive human resource management (HRM) in Pakistan. With over two decades of experience in the Disability Movement and a personal background of physical disability, the interviewee offers valuable insights into advancing disability inclusion through HRM policies and practices in the region. The interview delves into the advocate’s perspectives on various issues, including raising organisational awareness, recruiting and retaining employees with disabilities, identifying policy gaps, fostering effective collaborations between HR professionals and disability networks, and introducing innovations in workplace accommodations. It underscores the pressing need for tailored training, improved legal compliance, proactive accessibility audits and continuous engagement with the disability community to foster cultural shifts within organisations. The advocate substantiates her recommendations with illustrations of disability inclusion initiatives, such as internship programmes and partnerships with disabled people’s organisations, that serve as exemplary models. The interview provides a unique perspective on how South Asian and Pakistani organisations can harness the lived experiences and expertise of advocates to advance towards equitable, rights-based HRM that empowers employees with disabilities. The discussion provides valuable perspectives and insights for translating disability rights commitments into tangible strategies for HR practitioners.

Predicting legal case outcomes is a complex task due to the influence of multiple interrelated factors, including financial conditions, institutional characteristics, and legal dynamics. With the increasing availability of structured legal data, machine learning techniques provide an effective approach for supporting data-driven decision-making in this domain. This study evaluates the performance of four machine learning models—Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest—for predicting legal case outcomes. The dataset consists of structured features such as liability scores, financial indicators, and organizational attributes. Data preprocessing techniques, including handling missing values, feature selection, and one-hot encoding, were applied to prepare the dataset. The data was split using stratified sampling, and model performance was validated through 5-fold cross-validation. The models were evaluated using accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved the best overall performance, while Logistic Regression showed comparatively lower effectiveness. The findings highlight the importance of model selection and demonstrate the effectiveness of ensemble methods for structured legal data prediction.