The Experts below are selected from a list of 191703 Experts worldwide ranked by ideXlab platform

Wendy Harris - One of the best experts on this subject based on the ideXlab platform.

  • transitions of care after critical illness challenges to recovery and Adaptive Problem solving
    Critical Care Medicine, 2021
    Co-Authors: Kimberley J Haines, Elizabeth Hibbert, Nina Leggett, Leanne M Boehm, Tarli Hall, Rita N Bakhru, Anthony J Bastin, Brad W Butcher, Tammy L Eaton, Wendy Harris
    Abstract:

    Objectives: Investigate the challenges experienced by survivors of critical illness and their caregivers across the transitions of care from intensive care to community, and the potential Problem-solving strategies used to navigate these challenges. Design: Qualitative design—data generation via interviews and data analysis via the framework analysis method. Setting: Patients and caregivers from three continents, identified through the Society of Critical Care Medicine’s THRIVE international collaborative sites (follow-up clinics and peer support groups). Subjects: Patients and caregivers following critical illness. Interventions: Nil Measurements and Main Results: From 86 interviews (66 patients, 20 caregivers), we identified the following major themes: 1) Challenges for patients—interacting with the health system and gaps in care; managing others’ expectations of illness and recovery. 2) Challenges for caregivers—health system shortfalls and inadequate communication; lack of support for caregivers. 3) Patient and caregiver-driven Problem solving across the transitions of care—personal attributes, resources, and initiative; receiving support and helping others; and acceptance. Conclusions: Survivors and caregivers experienced a range of challenges across the transitions of care. There were distinct and contrasting themes related to the caregiver experience. Survivors and caregivers used comparable Problem-solving strategies to navigate the challenges encountered across the transitions of care.

Charles G Curtin - One of the best experts on this subject based on the ideXlab platform.

  • resilience design toward a synthesis of cognition learning and collaboration for Adaptive Problem solving in conservation and natural resource stewardship
    Ecology and Society, 2014
    Co-Authors: Charles G Curtin
    Abstract:

    Through the resilience design approach, I propose to extend the resilience paradigm by re-examining the components of Adaptive decision-making and governance processes. The approach can be divided into three core components: (1) equity design, i.e., the integration of collaborative approaches to conservation and Adaptive governance that generates effective self-organization and emergence in conservation and natural resource stewardship; (2) process design, i.e., the generation of more effective knowledge through strategic development of information inputs; and (3) outcome design, i.e., the pragmatic synthesis of the previous two approaches, generating a framework for developing durable and dynamic conservation and stewardship. The design of processes that incorporate perception and learning is critical to generating durable solutions, especially in developing linkages between wicked social and ecological challenges. Starting from first principles based on human cognition, learning, and collaboration, coupled with nearly two decades of practical experience designing and implementing ecosystem-level conservation and restoration programs, I present how design-based approaches to conservation and stewardship can be achieved. This context is critical in helping practitioners and resources managers undertake more effective policy and practice.

Kimberley J Haines - One of the best experts on this subject based on the ideXlab platform.

  • transitions of care after critical illness challenges to recovery and Adaptive Problem solving
    Critical Care Medicine, 2021
    Co-Authors: Kimberley J Haines, Elizabeth Hibbert, Nina Leggett, Leanne M Boehm, Tarli Hall, Rita N Bakhru, Anthony J Bastin, Brad W Butcher, Tammy L Eaton, Wendy Harris
    Abstract:

    Objectives: Investigate the challenges experienced by survivors of critical illness and their caregivers across the transitions of care from intensive care to community, and the potential Problem-solving strategies used to navigate these challenges. Design: Qualitative design—data generation via interviews and data analysis via the framework analysis method. Setting: Patients and caregivers from three continents, identified through the Society of Critical Care Medicine’s THRIVE international collaborative sites (follow-up clinics and peer support groups). Subjects: Patients and caregivers following critical illness. Interventions: Nil Measurements and Main Results: From 86 interviews (66 patients, 20 caregivers), we identified the following major themes: 1) Challenges for patients—interacting with the health system and gaps in care; managing others’ expectations of illness and recovery. 2) Challenges for caregivers—health system shortfalls and inadequate communication; lack of support for caregivers. 3) Patient and caregiver-driven Problem solving across the transitions of care—personal attributes, resources, and initiative; receiving support and helping others; and acceptance. Conclusions: Survivors and caregivers experienced a range of challenges across the transitions of care. There were distinct and contrasting themes related to the caregiver experience. Survivors and caregivers used comparable Problem-solving strategies to navigate the challenges encountered across the transitions of care.

Thomas J Dzurilla - One of the best experts on this subject based on the ideXlab platform.

  • Problem solving therapy for depression a meta analysis
    Clinical Psychology Review, 2009
    Co-Authors: Alissa C Bell, Thomas J Dzurilla
    Abstract:

    Problem-Solving Therapy (PST) is a cognitive-behavioral intervention that focuses on training in Adaptive Problem-solving attitudes and skills. The purpose of this paper was to conduct a meta-analysis of controlled outcome studies on efficacy of PST for reducing depressive symptomatology. Based on results involving 21 independent samples, PST was found to be equally effective as other psychosocial therapies and medication treatments and significantly more effective than no treatment and support/attention control groups. Moreover, component analyses indicated that PST is more effective when the treatment program includes (a) training in a positive Problem orientation (vs. Problem-solving skills only), (b) training in all four major Problem-solving skills (i.e., Problem definition and formulation, generation of alternatives, decision making, and solution implementation and verification), and (c) training in the complete PST package (Problem orientation plus the four Problem-solving skills).

Gratch, Jonathan Matthew - One of the best experts on this subject based on the ideXlab platform.

  • On efficient approaches to the utility Problem in Adaptive Problem solving
    2026
    Co-Authors: Gratch, Jonathan Matthew
    Abstract:

    Domain independent general purpose Problem solving techniques are desirable from the standpoints of software engineering and human computer interaction. They employ declarative and modular knowledge representations and present a constant homogeneous interface to the user, untainted by the peculiarities of the specific domain of interest. Unfortunately, this very insulation from domain details often precludes effective Problem solving behavior. General approaches have proven successful in complex real world situations only after a tedious cycle of manual experimentation and modification. Machine learning offers the prospect of automating this adaptation cycle, reducing the burden of domain-specific tuning and reconciling the conflicting needs of generality and efficacy. To date, however, the utility Problem--the realization that Adaptive strategies that were intended to improve Problem solving performance would actually degrade performance under difficult to predict circumstances--has impeded the development of Adaptive Problem solving techniques. Even systems designed to address the utility Problem can seriously impair Problem solving behavior, as they have incompletely accounted for the subtleties of the Problem. In order to develop a more rigorous approach to Adaptive Problem solving, this thesis details a formal framework that highlights these prior shortcomings, and presents a statistically rigorous solution to the utility Problem. Based on clearly articulated and well-motivated assumptions, this statistical method is applied successfully to learning heuristics for several artificial and a real-world Problem solving applications. Although the focus of this work is on Adaptive planning and scheduling, the results of this research have wider implications for operations research, software simulation, and decision-tree learning.U of I OnlyETDs are only available to UIUC Users without author permissio