Leveraging data analytics to optimize government interventions for homelessness, substance abuse, and mental health: A case study in evidence-based policy design
1 Department of Data Science, University of Salford, Manchester, United Kingdom.
2 Business Management, University of South Wales, Cardiff, Wales, United Kingdom.
3 Business, University of Tulsa, Tulsa, Oklahoma, United States.
4 College of Computing, Grandvalley State University, Allendale, Michigan, United States.
Research Article
World Journal of Advanced Research and Reviews, 2023, 20(03), 2025–2047
Publication history:
Received on 29 October 2023; revised on 09 December 2023; accepted on 12 December 2023
Abstract:
Introduction: Vulnerable groups facing the overlapping challenges of homelessness, substance abuse, and mental health issues form intricate social systems that require thoughtful analytical methods. Systematic barriers’ existence significantly contributes to inefficient healthcare and social services leading to complex problems that require complex response solutions. To understand how all these issues are interlinked, it becomes important to use some advanced analytical tools to determine what impacts such populations.
Materials and Methods: A systematic review methodology was used, incorporating different methods of analysis for existing literature as well as empirical research. By adapting synthesis multiple types, it was possible to obtain useful information from interdisciplinary fields of research through the use of complex bibliometric analyses. We used rationalism paradigms in this process erecting a literature review and meta-analysis paradigms alongside comparative case study analysis. For improved evidence quality and to minimize research bias we undertook a thorough scrutiny of diverse databases. All of these factors were tried out on the participants.
Results: Statistical analysis on the study's findings highlighted the intricate link between structural risk factors and intervention strategies. These investigations eventually revealed that the problems are complex and require multifaceted and connecting approaches. More precisely, we focused at the impact contexts of technology-based interventions and determined the multifaceted relations between social conditions, technological advancements and health care results. Theoretical models were explored more closely in order to gain more insight into the processes that lead to marginalization.
Discussion: Results on the study's findings highlighted the intricate link between structural risk factors and intervention strategies. Having analyze showed that there are always deeper aspects of the problems that require a complex, concurrent approach. More precisely, this work involved a review of literature towards identifying contextual factors that influence the effectiveness of the given intervention and how social determinants, technological advancement, and healthcare interplay. In light of this, theoretical models were analysed more thoroughly to provide insight into the processes that contribute to marginalization.
Conclusion: Addressing such social issues requires having a strategy and using all the available information. Technological, social, and empirical approaches, which are inclusive in the delivery of services, have promising potential in transforming service delivery. Applying technology-based collectively, it is possible to make the social equality actual and improve the condition of people living in poor environments.
Keywords:
Data Analytics; Social Interventions; Homelessness; Substance Abuse; Mental Health; Evidence-Based Policy; Government Services; Resource Allocation; Machine Learning; Vulnerable Populations; Data Integration; Policy Design; Bayesian Additive Regression Trees
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0