Bridging the gap: Exploring the barriers to using economic evidence in healthcare decision making and strategies for improving uptake

Evidence from economic evaluations is often not used to inform healthcare policy despite being well regarded by policy makers and physicians. This article employs the accessibility and acceptability framework to review the barriers to using evidence from economic evaluation in healthcare policy and the strategies used to overcome these barriers. Economic evaluations are often inaccessible to policymakers due to the absence of relevant economic evaluations, the time and cost required to conduct and interpret economic evaluations, and lack of expertise to evaluate quality and interpret results. Consistently reported factors that limit the translation of findings from economic evaluations into healthcare policy include poor quality of research informing economic evaluations, assumptions used in economic modelling, conflicts of interest, difficulties in transferring resources between sectors, negative attitudes to healthcare rationing, and the absence of equity considerations. Strategies to overcome these barriers have been suggested in the literature, including training, structured abstract databases, rapid evaluation, reporting checklists for journals, and considering factors other than cost effectiveness in economic evaluations, such as equity or budget impact. The factors that prevent or encourage decision makers to use evidence from economic evaluations have been identified, but the relative importance of these factors to decision makers is uncertain.

Decision making in social neurobiological systems modeled as transitions in dynamic pattern formation

Extant models of decision making in social neurobiological systems have typically explained task dynamics as characterized by transitions between two attractors. In this paper, we model a three-attractor task exemplified in a team sport context. The model showed that an attacker–defender dyadic system can be described by the angle x between a vector connecting the participants and the try line. This variable was proposed as an order parameter of the system and could be dynamically expressed by integrating a potential function. Empirical evidence has revealed that this kind of system has three stable attractors, with a potential function of the form V(x)=−k1x+k2ax2/2−bx4/4+x6/6, where k1 and k2 are two control parameters. Random fluctuations were also observed in system behavior, modeled as white noise εt, leading to the motion equation dx/dt = −dV/dx+Q0.5εt, where Q is the noise variance. The model successfully mirrored the behavioral dynamics of agents in a social neurobiological system, exemplified by interactions of players in a team sport.

Competition and its influence on consumer decision making in social marketing

This paper extends the largely conceptual understanding of competition in social marketing by empirically investigating, from a consumer perspective, the nature of competition and its influence on decision making at the individual level. Two phases of qualitative inquiry in Australia, comprising 30 and 20 semi-structured interviews respectively, examined the role of competition in young adults’ decision to adopt and maintain help-seeking for mental ill-health. The findings from thematic analysis suggest that competition operates at both the behavioural and goal level to influence consumers’ decision to perform behaviour and that the types of competition in operation may vary from the adoption to the maintenance of behaviour. The findings are integrated into a framework that social marketers could employ to identify, analyse and address competition.

Exploring variation in measurement as a foundation for statistical thinking in the elementary school

This study was based on the premise that variation is the foundation of statistics and statistical investigations. The study followed the development of fourth-grade students' understanding of variation through participation in a sequence of two lessons based on measurement. In the first lesson all students measured the arm span of one student, revealing pathways students follow in developing understanding of variation and linear measurement (related to research question 1). In the second lesson each student's arm span was measured once, introducing a different aspect of variation for students to observe and contrast. From this second lesson, students' development of the ability to compare their representations for the two scenarios and explain differences in terms of variation was explored (research question 2). Students' documentation, in both workbook and software formats, enabled us to monitor their engagement and identify their increasing appreciation of the need to observe, represent, and contrast the variation in the data. Following the lessons, a written student assessment was used for judging retention of understanding of variation developed through the lessons and the degree of transfer of understanding to a different scenario (research question 3).

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

Knowledge gain or system benefit in environmental decision making?

The quality of environmental decisions are gauged according to the management objectives of a conservation project. Management objectives are generally about maximising some quantifiable measure of system benefit, for instance population growth rate. They can also be defined in terms of learning about the system in question, in such a case actions would be chosen that maximise knowledge gain, for instance in experimental management sites. Learning about a system can also take place when managing practically. The adaptive management framework (Walters 1986) formally acknowledges this fact by evaluating learning in terms of how it will improve management of the system and therefore future system benefit. This is taken into account when ranking actions using stochastic dynamic programming (SDP). However, the benefits of any management action lie on a spectrum from pure system benefit, when there is nothing to be learned about the system, to pure knowledge gain. The current adaptive management framework does not permit management objectives to evaluate actions over the full range of this spectrum. By evaluating knowledge gain in units distinct to future system benefit this whole spectrum of management objectives can be unlocked. This paper outlines six decision making policies that differ across the spectrum of pure system benefit through to pure learning. The extensions to adaptive management presented allow specification of the relative importance of learning compared to system benefit in management objectives. Such an extension means practitioners can be more specific in the construction of conservation project objectives and be able to create policies for experimental management sites in the same framework as practical management sites.