Providing 'best practice' placements begins with mentoring skills : overview of the Program of Experience in the Palliative Approach (PEPA) quality improvement initiatives for mentors

Objectives PEPA is funded by the Department of Health and Ageing and aims to further improve the skill and confidence of the generalist workforce to work with people with palliative care needs. Recent quality improvement initiatives to promote transfer of learning into practice include appointment of a clinical educator, implementation of an online module for mentors and delivery of a mentoring workshop (collaborating with NSAP and PCC4U). This paper presents an overview of outcomes from these quality improvement initiatives. Methods PEPA host sites are selected based on their specialist palliative care level. Host site managers are surveyed six-monthly and participants are surveyed pre and three months post-placement to collect open and fixed response data on their experience of the program. Participants in the mentoring workshop (n=39) were asked to respond to a survey regarding the workshop outcomes. Results The percentage of placement participants who strongly agreed they ‘have the ability to implement the interventions required for people who have a life-limiting illness’ increased from 35% in 2011 (n=34) to 51% in 2012 (n=91) post-placement. Responses from mentor workshop participants indicated that 76% of respondents (n=25) agreed that they were able to identify principles for mentoring in the context of palliative care. In 2012, 61% of host site managers (n=54) strongly agreed that PEPA supports clinician working with people with a life-limiting illness. Conclusion Strategies to build the capabilities of palliative care professionals to mentor and support the learning experience of PEPA participants are critical to ongoing improvements of the program.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Dispute resolution across platforms : offshore gambling industry & EVE online

Contemporary online environments suffer from a regulatory gap; that is there are few options for participants between customer service departments and potentially expensive court cases in foreign jurisdictions. Whatever form of regulation ultimately fills that gap will be charged with determining whether specific behavior, within a specific environment, is fair or foul; whether it’s cheating or not. However, cheating is a term that, despite substantial academic study, remains problematic. Is anything the developer doesn’t want you to do cheating? Is it only if your actions breach the formal terms of service? What about the community norms, do they matter at all? All of these remain largely unresolved questions, due to the lack of public determination of cases in such environments, which have mostly been settled prior to legal action. In this paper, I propose a re-branding of participant activity in such environments into developer-sanctioned, advantage play, and cheating. Advantage play, ultimately, is activity within the environment in which the player is able to turn the mechanics of the environment to their advantage without breaching the rules of the environment. Such a definition, and the term itself, is based on the usage of the term within the gambling industry, in which advantage play is considered betting with the advantage in the players’ favor rather than that of the house. Through examples from both the gambling industry and the Massively Multiplayer Role-Playing Game Eve Online, I consider the problems in defining cheating, suggest how the term ‘advantage play’ may be useful in understanding participants behavior in contemporary environments, and ultimately consider the use of such terminology in dispute resolution models which may overcome this regulatory gap.