SHIFTING SOCIAL PREFERENCES TOWARDS MORAL PREJUDICES IN A PROCESS OF RESOURCE ALLOCATION DECISION MAKING

This paper presents an analysis of the main theories and results of experimental research in the context of the shifting social preferences towards moral prejudices in a process of resource allocation decision making. Researchers of game theory have found that three motives are included in the decision-making process about resource allocation: social preferences, moral prejudices, and self-interest. Personal interests and moral prejudices are strong predictors in the model of predicting people's social orientations. Moral prejudices, being the distortions created by self-interest, can strongly influence people's social preferences, and even change them to the opposite. As a result, an asymmetric relationship is established between personal interest and moral prejudices in the decision-making process about resource allocation. When moral prejudices become an obstacle to achieving a goal, a person unconsciously distorts the information so that it justifies its actions. These distortions can be manifested in the avoidance of information that interferes with personal interests, the selective selection of information, and even recourse to opposing moral principles. In the long run, all this is expressed in the change of a person's social orientation from altruistic to selfish. These changes in people's social preferences are confirmed by the results of numerous experiments not only in social psychology, but also in social neuropsychology and neuroeconomics. The way to overcome these distortions is to have a clear understanding of the limits of personal interests and an understanding of one's own motives in decisions about resource allocation.

Case Manager Resource Allocation Decision-Making for Adult Home Care Clients: With Comparisons to a High Needs Pediatric Home Care Clients

Home care programs have become integral parts of the overall health service system in Canada and in many other developed nations. Resource allocation decision-making by home care case managers (CM) is a complex task where CMs are challenged to meet the dual responsibilities for clients, in order that they achieve high quality care, and to the system to contain costs. The purpose of this study was to extend what is known about resource allocation decision-making factors identified in a previous systematic literature review and ethnographic study within a high needs pediatric context conducted by the principal investigator in Western Canada. Spradley’s ethnoscience method was used in this research. The study sample consisted of 17 home care CMs, professional practice leads, and their managers from two separate home care offices. All participating CMs had assigned caseloads and were involved in the assessment and implementation of care planning for clients. Purposive sampling methods were employed. In keeping with Spradley’s ethnoscience approach, data collection occurred in three distinct phases or rounds. The first round of data collection began with a series of one-on-one interviews with card sorts, the second round of data collection was another series of one-on-one interviews with CMs who were not interviewed in the prior round, and the third and final round of data collection was a focus group to accomplish further refinement and verification of our established categories. Participants identified five categories of factors that effected their resource allocation decision-making. The categories were related to one of five main areas: the client, the CM, the home care program, community resources, or the health care system. The findings of this study reinforced the complexity of CM resource allocation decision-making in home care. This study provides new insights into CM resource allocation decision-making based on multidisciplinary, integrated home care teams caring for adults, the majority of whom are 65 years and older. This study also provides the comparison of taxonomy that differs between pediatric and adult home care populations that influence resource allocation decision-making.