Mental health nurses’ attitudes, behaviour, experience and knowledge regarding adults with a diagnosis of borderline personality disorder: systematic, integrative literature review

Aims and objectives To establish whether mental health nurses responses to people with borderline personality disorder are problematic and, if so, to inform solutions to support change. Background There is some evidence that people diagnosed with borderline personality disorder are unpopular among mental health nurses who respond to them in ways which could be counter-therapeutic. Interventions to improve nurses’ attitudes have had limited success. Design Systematic, integrative literature review. Methods Computerised databases were searched from inception to April 2015 for papers describing primary research focused on mental health nurses’ attitudes, behaviour, experience, and knowledge regarding adults diagnosed with borderline personality disorder. Analysis of qualitative studies employed metasynthesis; analysis of quantitative studies was informed by the theory of planned behaviour. Results Forty studies were included. Only one used direct observation of clinical practice. Nurses’ knowledge and experiences vary widely. They find the group very challenging to work with, report having many training needs, and, objectively, their attitudes are poorer than other professionals’ and poorer than towards other diagnostic groups. Nurses say they need a coherent therapeutic framework to guide their practice, and their experience of caregiving seems improved where this exists. Conclusions Mental health nurses’ responses to people with borderline personality disorder are sometimes counter-therapeutic. As interventions to change them have had limited success there is a need for fresh thinking. Observational research to better understand the link between attitudes and clinical practice is required. Evidence-based education about borderline personality disorder is necessary, but developing nurses to lead in the design, implementation and teaching of coherent therapeutic frameworks may have greater benefits. Relevance to clinical practice There should be greater focus on development and implementation of a team-wide approach, with nurses as equal partners, when working with patients with borderline personality disorder.

Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds

In the last thirty years, the emergence and progression of biologging technology has led to great advances in marine predator ecology. Large databases of location and dive observations from biologging devices have been compiled for an increasing number of diving predator species (such as pinnipeds, sea turtles, seabirds and cetaceans), enabling complex questions about animal activity budgets and habitat use to be addressed. Central to answering these questions is our ability to correctly identify and quantify the frequency of essential behaviours, such as foraging. Despite technological advances that have increased the quality and resolution of location and dive data, accurately interpreting behaviour from such data remains a challenge, and analytical methods are only beginning to unlock the full potential of existing datasets. This review evaluates both traditional and emerging methods and presents a starting platform of options for future studies of marine predator foraging ecology, particularly from location and two-dimensional (time-depth) dive data. We outline the different devices and data types available, discuss the limitations and advantages of commonly-used analytical techniques, and highlight key areas for future research. We focus our review on pinnipeds - one of the most studied taxa of marine predators - but offer insights that will be applicable to other air-breathing marine predator tracking studies. We highlight that traditionally-used methods for inferring foraging from location and dive data, such as first-passage time and dive shape analysis, have important caveats and limitations depending on the nature of the data and the research question. We suggest that more holistic statistical techniques, such as state-space models, which can synthesise multiple track, dive and environmental metrics whilst simultaneously accounting for measurement error, offer more robust alternatives. Finally, we identify a need for more research to elucidate the role of physical oceanography, device effects, study animal selection, and developmental stages in predator behaviour and data interpretation.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.