Exploring the Unmet Needs of Primary Caregivers of Autistic Children and Its Implications for Social Work Practice in Ghana

Caring for an autistic child is fraught with various difficulties and may present unmet needs that could affect the overall well‐being of caregivers and children themselves. Consequently, gaining insight into the unmet needs of these caregivers is imperative for the development of targeted and effective interventions to enhance their quality of life and improve their ability to care for their children. Using a descriptive qualitative research design, this study engaged 10 primary caregivers of autistic children in Ghana to understand their unmet needs. Data were collected through in‐depth interviews and thematically analyzed. The analysis revealed the urgent need for financial support for primary caregivers, the availability of more special schools, and the services of trained professionals in the field of autism. Caregivers also called for the intensification of public education to help reorient the perspectives of the general population on the autism condition. Based on the findings, some recommendations for policy and practice were made. The implications of the findings for social work are also discussed

A heterogeneous graph-based semi-supervised learning framework for access control decision-making

For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security. This paper proposes a novel semi-supervised learning framework that leverages heterogeneous graph neural network-based embedding to encapsulate both the intricate relationships within the organizational structure and interactions between users and resources. Unlike existing methods focusing solely on individual user and resource attributes, our approach embeds organizational and operational interrelationships into the hidden layer node embeddings. These embeddings are learned from a self-supervised link prediction task based on a constructed access control heterogeneous graph via a heterogeneous graph neural network. Subsequently, the learned node embeddings, along with the original node features, serve as inputs for a supervised access control decision-making task, facilitating the construction of a machine-learning access control model. Experimental results on the open-sourced Amazon access control dataset demonstrate that our proposed framework outperforms models using original or manually extracted graph-based features from previous works. The prepossessed data and codes are available on GitHub,facilitating reproducibility and further research endeavors