Quantifying Dynamic Resilience using First-process Autoregressive Modelling: An Empirical Study

Dynamic resilience is a temporal process that reflects individuals’ capability to overcome task-induced stress and sustain their performance during task-related events. First-order autoregressive (AR(1)) modelling is posited for measuring individuals’ dynamic resilience over time. The current research investigated this by testing 30 adults in a dynamic decision-making task. AR(1) modelling was conducted on the data, and was compared against a modified seismic resilience metric for concurrent validity purposes. Results revealed that AR(1) modeled parameters are applicable in assessing participants’ dynamic resilience, with analyses supporting their use to distinguish between individuals that can overcome task-induced stress and those that cannot, as well as, in the classification of individuals’ dynamic resilience.

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A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

Medical Decision Making ◽

10.1177/0272989x18801312 ◽

2018 ◽

Vol 38 (8) ◽

pp. 904-916 ◽

Cited By ~ 8

Author(s):

Aasthaa Bansal ◽

Patrick J. Heagerty

Keyword(s):

Decision Making ◽

Statistical Methods ◽

Sensitivity And Specificity ◽

Prognostic Model ◽

Dynamic Decision Making ◽

Time Dependent ◽

Survival Models ◽

Biliary Cirrhosis ◽

Time Varying ◽

Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

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Designing for Accelerated Translation (DART) of emerging innovations in health

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.386 ◽

2019 ◽

Vol 3 (2-3) ◽

pp. 53-58 ◽

Cited By ~ 4

Author(s):

Alex T. Ramsey ◽

Enola K. Proctor ◽

David A. Chambers ◽

Jane M. Garbutt ◽

Sara Malone ◽

...

Keyword(s):

Decision Making ◽

Health System ◽

Dynamic Process ◽

Evidence Base ◽

Dynamic Decision Making ◽

Dissemination And Implementation ◽

The Past ◽

Learning Health System ◽

Stakeholder Partnerships ◽

Over Time

AbstractAccelerating innovation translation is a priority for improving healthcare and health. Although dissemination and implementation (D&I) research has made significant advances over the past decade, it has attended primarily to the implementation of long-standing, well-established practices and policies. We present a conceptual architecture for speeding translation of promising innovations as candidates for iterative testing in practice. Our framework to Design for Accelerated Translation (DART) aims to clarify whether, when, and how to act on evolving evidence to improve healthcare. We view translation of evidence to practice as a dynamic process and argue that much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation. The DART framework proposes that additional factors – demand, risk, and cost, in addition to the evolving evidence base – should influence the pace of translation over time. Attention to these underemphasized factors may lead to more dynamic decision-making about whether or not to adopt an emerging innovation or de-implement a suboptimal intervention. Finally, the DART framework outlines key actions that will speed movement from evidence to practice, including forming meaningful stakeholder partnerships, designing innovations for D&I, and engaging in a learning health system.

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Awareness and Decision Making in Professional Ethics

European Psychologist ◽

10.1027//1016-9040.5.1.19 ◽

2000 ◽

Vol 5 (1) ◽

pp. 19-27 ◽

Cited By ~ 2

Author(s):

Ronny Swain

Keyword(s):

Decision Making ◽

Professional Ethics ◽

Ethical Decision Making ◽

Code Of Ethics ◽

Professional Autonomy ◽

Ethical Awareness ◽

Ethical Decisions ◽

The Right ◽

Aid Decision

The paper describes the development of the 1998 revision of the Psychological Society of Ireland's Code of Professional Ethics. The Code incorporates the European Meta-Code of Ethics and an ethical decision-making procedure borrowed from the Canadian Psychological Association. An example using the procedure is presented. To aid decision making, a classification of different kinds of stakeholder (i.e., interested party) affected by ethical decisions is offered. The author contends (1) that psychologists should assert the right, which is an important aspect of professional autonomy, to make discretionary judgments, (2) that to be justified in doing so they need to educate themselves in sound and deliberative judgment, and (3) that the process is facilitated by a code such as the Irish one, which emphasizes ethical awareness and decision making. The need for awareness and judgment is underlined by the variability in the ethical codes of different organizations and different European states: in such a context, codes should be used as broad yardsticks, rather than precise templates.

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