Crowding out or crowding in informal safety nets? : the role of formal social protection targeted at addressing child poverty in South Africa

Formal social protection in the form of cash transfers has been adopted as one of the strategies for addressing child poverty in South Africa. The Child Support Grant (CSG) established in 1998, is the largest social assistance programme in South Africa in terms of coverage and is targeted at poor children through the primary caregiver. Prior to the grant, the majority of poor children in South Africa lacked access to formal social protection and mostly relied on informal safety nets provided by extended family, neighbours and community members. In view of the critical role that informal safety nets play in the provision of social support to poor children and for the CSG to produce maximum benefits for the recipients, the grant needs to be designed in a way that builds on pre-existing informal safety nets rather than displacing them. The purpose of this thesis is to investigate the nature of the interaction between formal social protection, specifically the CSG, and informal safety nets, and to examine whether the grant displaces, ‘crowds out’ or strengthens, ‘crowds in’ the various forms of informal safety nets. The thesis also provides suggestions on how social protection measures might be designed to crowd in more informal support. The thesis is a literature review based study and involves systematic identification, selection and assessment of relevant texts. In terms of the theoretical framework, the study considers exchange versus altruism as motives for provision of informal support. Findings of the study seem to suggest that the grant had both positive and negative effects on the various forms of informal safety nets, albeit mostly modest effects, which might be due to the low value of the grant. The majority of the reviewed texts reported a slight increase in the social status of the grant recipients due to access to the CSG and some improvements in their capacity to borrow, lend and pool resources within the extended family and community. On the other hand, several texts reported a decrease in cash gifts and also father child support as a result of receiving the grant. A few texts in the review discovered modest positive effects on CSG recipients’ membership in stokvels compared to non-recipients of similar socio-economic status. Modest to no effects were also documented for the grant’s effects on child care and living arrangements. Generally, the findings of the review suggest that the effects of the grant, both positive and negative were quite modest to result in significant crowding out or crowding in of informal safety nets in South Africa. The thesis suggests caregiver support and use of conditionalties as possible designs to crowd in informal safety nets.Master in International Social Welfare and Health Polic

The Influence of NegEx on ICD-10 Code Prediction in Swedish: How is the Performance of BERT and SVM Models Affected by Negations?

Clinical text contains many negated concepts since the physician excludes irrelevant symptoms when reasoning and concluding about the diagnosis. This study investigates the machine interpretation of negated symptoms and diagnoses using a rule-based negation detector and its influence on downstream text classification task. The study focuses on the effect of negated concepts and NegEx preprocessing on classifier performance for predicting ICD-10 gastro surgical codes assigned to discharge summaries. Based on the experiments, NegEx preprocessing resulted in a slight performance improvement for traditional machine learning model (SVM) and had no effect on the performance of the deep learning model KB/BERT

Challenges and opportunities beyond structured data in analysis of electronic health records

Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time-consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well-designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text

Implementation of a distributed guideline-based decision support model within a patient-guidance framework

We report on new projection engine which was developed in order to implement a distributed guideline-based decision support system (DSS) within the European project MobiGuide.In this model, small portions of the guideline knowledge are projected, i.e. 'downloaded', from a central DSS server to a local DSS in the patient's mobile device, which then applies that knowledge using the mobile device’s local resources. Furthermore, the projection engine generates guideline projections which are adapted to the patient’s previously defined preferences and, implicitly, to the patient’s current context, which is embodied in the projected knowledge. We evaluated this distributed guideline application model for two complex guidelines: one for Gestational Diabetes Mellitus, and one for Atrial Fibrillation. We found that the initial specification of what we refer to as the customized guideline should be in the terms of the distributed DSS, i.e., include two levels: one for the central DSS, and one for the local DSS. In addition, we found significant differences between the customized, distributed versions of the two guidelines, indicating further research directions and possibly additional ways to analyze and characterize guidelines

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting

Design and Evaluation of a Pervasive Coaching and Gamification Platform for Young Diabetes Patients

Self monitoring, personal goal-setting and coaching, education and social support are strategies to help patients with chronic conditions in their daily care. Various tools have been developed, e.g., mobile digital coaching systems connected with wearable sensors, serious games and patient web portals to personal health records, that aim to support patients with chronic conditions and their caregivers in realizing the ideal of self-management. We describe a platform that integrates these tools to support young patients in diabetes self-management through educational game playing, monitoring and motivational feedback. We describe the design of the platform referring to principles from healthcare, persuasive system design and serious game design. The virtual coach is a game guide that can also provide personalized feedback about the user’s daily care related activities which have value for making progress in the game world. User evaluations with patients under pediatric supervision revealed that the use of mobile technology in combination with web-based elements is feasible but some assumptions made about how users would connect to the platform were not satisfied in reality, resulting in less than optimal user experiences. We discuss challenges with suggestions for further development of integrated pervasive coaching and gamification platforms in medical practice

Anatomy of Alterity: Instrumental Identies Among the San in Zimbabwe

This is a study on identity politics as bases for alterity. The term ‘alterity’ is here used to mean the state of being regarded, or regarding oneself as the ‘Other’. The term therefore carries subordinate status implications. We assume and seek to show that alterity is a complex process, hence our metaphorical use of the term ‘anatomy’. The study focuses on the economically and socially marginal San, an autochthonous ethnic group in Western Zimbabwe. Primarily, it seeks to show that ethnic identity is a social construct that dominant and subordinate groups use in their interaction. In sociology, we are aware that labelling suggests the contours of power in social relationships. We go beyond this structuralist position to argue that identity is subject to time, place and context and subordinate groups use their ‘given’ identities instrumentally to access vantage points, in the case of the San to be identifiable to local and external benefactors. This expediency is an effective weapon of the weak; it averts unnecessary and dangerous confrontation and keeps them as prime candidates for outside help. In other contexts, the young San in particular shed off their identity and adopt that of the dominant groups in a bid to level off the playing field of life’s opportunities. We also show that such stratagems are not unique to subordinate groups; regardless of structural position, people instrumentally use their identity to improve their life chances. A key argument of the article is that ethnicity is a transitional identity employed and dispensed with when convenient

A context-sensitive framework for mobile terminals for assisting Type 2 diabetes patients

PURPOSE The aim of this research is to develop a mobile-based framework for using context information to create an intelligent environment within which applications for diabetes self-management operate. MOTIVATION Mobile applications are emerging as a preferred method for diabetes self-management. This increases the number of interruptions that the patient will get from the phone and can easily become annoying. Using context information holds a potential for enhancing usability. METHODS A prototype was constructed that uses a step-counter, calendar, microphone, camera, battery and onboard clock as sources of potentially useful context information. Controlled experiments were conducted to test the accuracy of context detection and the use of limited mobile resources. RESULTS The framework provided an efficient way of detecting contexts within an acceptable level of accuracy. The effect on mobile resources was almost insignificant; with effect on battery less than 15% and occupying less than 1% of main memory. CONCLUSION Context information can be used to enhance usability of disease management applications on mobile phones. Modelling context information using the proposed generalizable, reusable and extensible abstraction in combination with an event-driven architecture was suitable for mobile phones because it lowered the time and space complexity and also solves the problem of modelling context information from heterogeneous data sources

Crowding out or crowding in informal safety nets? : the role of formal social protection targeted at addressing child poverty in South Africa

Formal social protection in the form of cash transfers has been adopted as one of the strategies for addressing child poverty in South Africa. The Child Support Grant (CSG) established in 1998, is the largest social assistance programme in South Africa in terms of coverage and is targeted at poor children through the primary caregiver. Prior to the grant, the majority of poor children in South Africa lacked access to formal social protection and mostly relied on informal safety nets provided by extended family, neighbours and community members. In view of the critical role that informal safety nets play in the provision of social support to poor children and for the CSG to produce maximum benefits for the recipients, the grant needs to be designed in a way that builds on pre-existing informal safety nets rather than displacing them. The purpose of this thesis is to investigate the nature of the interaction between formal social protection, specifically the CSG, and informal safety nets, and to examine whether the grant displaces, ‘crowds out’ or strengthens, ‘crowds in’ the various forms of informal safety nets. The thesis also provides suggestions on how social protection measures might be designed to crowd in more informal support. The thesis is a literature review based study and involves systematic identification, selection and assessment of relevant texts. In terms of the theoretical framework, the study considers exchange versus altruism as motives for provision of informal support. Findings of the study seem to suggest that the grant had both positive and negative effects on the various forms of informal safety nets, albeit mostly modest effects, which might be due to the low value of the grant. The majority of the reviewed texts reported a slight increase in the social status of the grant recipients due to access to the CSG and some improvements in their capacity to borrow, lend and pool resources within the extended family and community. On the other hand, several texts reported a decrease in cash gifts and also father child support as a result of receiving the grant. A few texts in the review discovered modest positive effects on CSG recipients’ membership in stokvels compared to non-recipients of similar socio-economic status. Modest to no effects were also documented for the grant’s effects on child care and living arrangements. Generally, the findings of the review suggest that the effects of the grant, both positive and negative were quite modest to result in significant crowding out or crowding in of informal safety nets in South Africa. The thesis suggests caregiver support and use of conditionalties as possible designs to crowd in informal safety nets

Complex Network Structure Patterns in Open Internet Communities for People with Diabetes

Type 2 diabetes is one of the greatest challenges that continues to grow because of the ageing population, increasing morbid obesity and sedentary lifestyles. Social media such as Facebook and YouTube have transformed the way people interact in general and on the Internet, but the role of social media in healthcare is still not well-understood. Current understanding of the association between user interaction patterns and health outcomes or behaviour change is still limited. In this dissertation I present a framework, based on social network analysis, to explore the nature of patient interactions in online communities. Results show that people with diabetes join online communities typically immediately following diagnosis, with over 80% of the patients having being diagnosed in under 2 years. The networks are very centralized with continually shrinking density and diameter as the the networks grow, and these results directly contrast with current evidence about non-healthcare social networks. Further, using machine learning techniques, I show that we can predict health outcomes such as weight loss performance based on how the patients interact online. The results have practical relevance for understanding the nature of patients interactions, as well as for designing personalized diabetes interventions based on emergent social technologies