Moral shielding: A grounded theory of integrity maintenance in multidisciplinary teams

Using a classic grounded theory methodology (CGT), this study explores the phenomenon of moral shielding within mental health multidisciplinary teams (MDTS). The study was located within three catchment areas engaged in acute mental health service practice. The main concern identified was the maintenance of a sense of personal integrity during situational binds. Through theoretical sampling thirty two practitioners, including; doctors, nurses, social workers, occupational therapists, counsellors and psychologists, where interviewed face to face. In addition, emergent concepts were identified through observation of MDTs in clinical and research practice. Following a classic grounded theory methodology, data collection and analysis occurred simultaneously. A constant comparative approach was adopted and resulted in the immergence of three sub- core categories; moral abdication, moral hinting and pseudo-compliance. Moral abdication seeks to re-position within an event in order to avoid or deflect the initial obligation to act, it is a strategy used to remove or reduce moral ownership. Moral gauging represents the monitoring of an event with the goal of judging the congruence of personal principles and commitments with that of other practitioners. This strategy is enacted in a bid to seek allies for the support of a given moral position. Pseudo-compliance represents behaviour that hides desired principles and commitments in order to shield them from challenge. This strategy portrays agreement with the dominant position within the MDT, whilst holding a contrary position. It seeks to preserve a reservoir of emotional energy required to maintain a sense of personal integrity. Practitioners who were successful in enacting moral shielding were found to not experience significant emotional distress associated with the phenomenon of moral distress; suggesting that these practitioners had found mechanisms to manage situational binds that threatened their sense of personal integrity.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.