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Problem-Solving Theory: The Task-Centred Model

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• First Online: 12 April 2022
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• Blanca M. Ramos 5 &
• Randall L. Stetson 6  

Part of the book series: Social Work ((SOWO))

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This chapter examines the task-centred model to illustrate the application of problem-solving theory for social work intervention. First, it provides a brief description of the problem-solving model. Its historical development and key principles and concepts are presented. Next, the chapter offers a general overview of the crisis intervention model. The task-centred model and crisis intervention share principles and methods drawn from problem-solving theory. The remainder of the chapter focuses on the task-centred model. It reviews its historical background, viability as a framework for social work generalist practice, as well as its applicability with diverse client populations and across cultural settings. The structured steps that guide task-centred implementation throughout the helping process are described. A brief critical review of the model’s strengths and limitations is provided. The chapter concludes with a brief summary and some closing thoughts.

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State University of New York at Albany, Albany, NY, USA

Blanca M. Ramos

State University of New York at Oswego, Oswego, NY, USA

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Correspondence to Blanca M. Ramos .

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School of Human Services and Social Work, Griffith University, Meadowbrook, QLD, Australia

Dorothee Hölscher

School of Social Sciences, UNSW Sydney, Sydney, NSW, Australia

Richard Hugman

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Ramos, B.M., Stetson, R.L. (2022). Problem-Solving Theory: The Task-Centred Model. In: Hölscher, D., Hugman, R., McAuliffe, D. (eds) Social Work Theory and Ethics. Social Work. Springer, Singapore. https://doi.org/10.1007/978-981-16-3059-0_9-1

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DOI : https://doi.org/10.1007/978-981-16-3059-0_9-1

Received : 24 December 2021

Accepted : 25 January 2022

Published : 12 April 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-16-3059-0

Online ISBN : 978-981-16-3059-0

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THEORIES AND PRINCIPLES OF PROBLEM SOLVING IN MATHEMATICS

2023, Article

Doing mathematics means that students are engaged in learning mathematics through reasoning and problem solving (NCTM, 2014). Prospective mathematics teachers need to learn about how to engage students in solving and talking about tasks that can be tackled in different ways by different students. Mathematically, proficient students are able to make sense of a situation, select solution paths, consider alternative strategies and monitor their progress (CCSSO, 2010). Before we can be effective in teaching mathematics, we need to have a good knowledge about what we are supposed to be teaching and how students learn mathematics. We are familiar with why we teach mathematics at the basic and high schools.

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Statistics > Machine Learning

Title: data science principles for interpretable and explainable ai.

Abstract: Society's capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible software. As capabilities have expanded, so have risks, with models often deployed without fully understanding their potential impacts. Interpretable and interactive machine learning aims to make complex models more transparent and controllable, enhancing user agency. This review synthesizes key principles from the growing literature in this field. We first introduce precise vocabulary for discussing interpretability, like the distinction between glass box and explainable algorithms. We then explore connections to classical statistical and design principles, like parsimony and the gulfs of interaction. Basic explainability techniques -- including learned embeddings, integrated gradients, and concept bottlenecks -- are illustrated with a simple case study. We also review criteria for objectively evaluating interpretability approaches. Throughout, we underscore the importance of considering audience goals when designing interactive algorithmic systems. Finally, we outline open challenges and discuss the potential role of data science in addressing them. Code to reproduce all examples can be found at this https URL .

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