Using Workload Measurement Tools in Diverse Care Contexts: the Experience of Staff in Mental Health and Learning Disability Inpatient Settings

Introduction The critical challenge of determining the correct level and skill-mix of nursing staff required to deliver safe and effective healthcare has become an international concern. It is recommended that evidence-based staffing decisions are central to the development of future workforce plans. Workforce planning in mental health and learning disability nursing is largely under-researched with few tools available to aid the development of evidence-based staffing levels in these environments. Aim It was the aim of this study to explore the experience of staff using the Safer Nursing Care Tool (SNCT) and the Mental Health and Learning Disability Workload Tool (MHLDWT) in mental health and learning disability environments. Method Following a 4-week trial period of both tools a survey was distributed via Qualtrics on-line survey software to staff members who used the tools during this time. Results The results of the survey revealed that the tools were considered a useful resource to aid staffing decisions; however specific criticisms were highlighted regarding their suitability to psychiatric intensive care units (PICU) and learning disability wards. Discussion This study highlights that further development of workload measurement tools is required to support the implementation of effective workforce planning strategies within mental health and learning disability services. Implications for Practice With increasing fiscal pressures the need to provide cost-effective care is paramount within NHS services. Evidence-based workforce planning is therefore necessary to ensure that appropriate levels of staff are determined. This is of particular importance within mental health and learning disability services due to the reduction in the number of available beds and an increasing focus on purposeful admission and discharge.

Energy Management in UK Non-Domestic Buildings - a New Perspective to Provoke Reduced Carbon Emissions

Politicians, industry and the public generally accept the need for energy consumption to be cut to deliver climate change mitigation measures essential for us to avoid climate disaster. For non-domestic fuel users current energy policy has attempted to drive this through rational economic responses to energy cost pressures. This reliance on voluntary action has created an “Energy Inconsistency”, that is a marked difference between energy opportunities that have been proven technically viable, financially rational and retrofit feasible and those actually adopted. Other factors must therefore be involved to influence what appear to be simple carbon and cost saving opportunities. This paper presents a new approach to energy efficiency and consumption in non-domestic buildings, viewing attitudes and behaviours of building owners and users as the key driver of energy consumption. A new framework is proposed as a method to examine the impact of building ownership on the users’ and owners’ abilities to improve energy efficiency and consumption and identify opportunities to overcome the barriers inherent in these ownership structures.

Investigating Behaviour and Face Encoding in a Hypothetical Real-World Social Contract: Handwashing in Hazardous Health Settings

Investigations into the evolutionary origins of human cognition has shown that individuals’ memory for others is influenced by the latter’s behaviour in social contracts. Such research is primarily based on hypothetical or more abstract forms of social contracts, whereas an application of this knowledge to everyday health behaviours can be of great value. To address this, the current study investigated whether participants who were asked to imagine themselves in a hypothetical hazardous health scenario showed differential response sensitivity (d’) and latency (RT) to faces of hospital staff tagged with contrasting hand hygiene before touching patients: clean hands, dirty hands, or unknown hand-washing behaviour (control). The test used a two alternative forced-choice (2AFC: “old/new”) face recognition paradigm. The findings showed that d’ to dirty and clean hands was similar, but higher than for controls. Moreover, d’ was not affected by the occupation of hospital staff (nurses vs porters). The absence of memory gains towards clean or dirty hands points to the need for new strategies to remind patients to observe (and remember) the hand hygiene of others when exposed to hazardous health environments.

Personality and performance on digit span tests in online and offline settings.

This paper describes two studies examining links between personality and performance on a cognitive test in online and laboratory settings. Study 1 was completed online. 345 participants passively recruited through a personality assessment website completed a Five Factor Model personality inventory derived from the International Personality Item Pool. They then completed an online text-based digit span test. This required participants to repeat increasingly longer strings of digits, either in the same order (forward) or in the opposite of the presentation order (reverse). Conventional digit span tasks ask participants to respond verbally; in this instance they responded by typing the digits. Agreeableness and Openness to Experience each had small but significant associations with forward and reverse digit span. In a second, laboratory based, study, 103 participants completed paper versions of the IPIP Five Factor inventory, the NEO-FFI, and a battery of cognitive tests including the WAIS 4 digit span test. In this instance, Agreeableness and Openness to Experience were not significantly correlated with digit span measures. Taken together, these studies suggest that personality characteristics may influence performance on an online cognitive test. This effect was not seen in an offline version of the study. The paper will consider potential implications for online testing, for equivalence of online and offline methods, and for links between personality and performance on this cognitive test.

Applying gaze-contingent training within community settings to infants from diverse SES backgrounds

Even in infancy children from low-SES backgrounds differ in frontal cortex functioning and, by the start of preschool, they frequently show poor performance on executive functions including attention control. These differences may causally mediate later difficulties in academic learning. Here, we present a study to assess the feasibility of using computerized paradigms to train attention control in infants, delivered weekly over five sessions in early intervention centres for low-SES families. Thirty-three 12-month-old infants were recruited, of whom 23 completed the training. Our results showed the feasibility of repeat-visit cognitive training within community settings. Training-related improvements were found, relative to active controls, on tasks assessing visual sustained attention, saccadic reaction time, and rule learning, whereas trend improvements were found on assessments of short-term memory. No significant improvements were found in task switching. These results warrant further investigation into the potential of this method for targeting ‘at-risk’ infants in community settings.

SCADA House Intelligent Management for Energy Efficiency Analysis in Domestic Consumers

The implementation of smart homes allows the domestic consumer to be an active player in the context of the Smart Grid (SG). This paper presents an intelligent house management system that is being developed by the authors to manage, in real time, the power consumption, the micro generation system, the charge and discharge of the electric or plug-in hybrid vehicles, and the participation in Demand Response (DR) programs. The paper proposes a method for the energy efficiency analysis of a domestic consumer using the SCADA House Intelligent Management (SHIM) system. The main goal of the present paper is to demonstrate the economic benefits of the implemented method. The case study considers the consumption data of some real cases of Portuguese house consumption over 30 days of June of 2012, the Portuguese real energy price, the implementation of the power limits at different times of the day and the economic benefits analysis.

Real-Time Simulation of energy Management in a Domestic Consumer

Recent and future changes in power systems, mainly in the smart grid operation context, are related to a high complexity of power networks operation. This leads to more complex communications and to higher network elements monitoring and control levels, both from network’s and consumers’ standpoint. The present work focuses on a real scenario of the LASIE laboratory, located at the Polytechnic of Porto. Laboratory systems are managed by the SCADA House Intelligent Management (SHIM), already developed by the authors based on a SCADA system. The SHIM capacities have been recently improved by including real-time simulation from Opal RT. This makes possible the integration of Matlab®/Simulink® real-time simulation models. The main goal of the present paper is to compare the advantages of the resulting improved system, while managing the energy consumption of a domestic consumer.

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Defining risk acceptance criteria in occupational settings: A case study in the furniture industrial sector

The use of appropriate acceptance criteria in the risk assessment process for occupational accidents is an important issue but often overlooked in the literature, particularly when new risk assessment methods are proposed and discussed. In most cases, there is no information on how or by whom they were defined, or even how companies can adapt them to their own circumstances. Bearing this in mind, this study analysed the problem of the definition of risk acceptance criteria for occupational settings, defining the quantitative acceptance criteria for the specific case study of the Portuguese furniture industrial sector. The key steps to be considered in formulating acceptance criteria were analysed in the literature review. By applying the identified steps, the acceptance criteria for the furniture industrial sector were then defined. The Cumulative Distribution Function (CDF) for the injury statistics of the industrial sector was identified as the maximum tolerable risk level. The acceptable threshold was defined by adjusting the CDF to the Occupational, Safety & Health (OSH) practitioners’ risk acceptance judgement. Adjustments of acceptance criteria to the companies’ safety cultures were exemplified by adjusting the Burr distribution parameters. An example of a risk matrix was also used to demonstrate the integration of the defined acceptance criteria into a risk metric. This work has provided substantial contributions to the issue of acceptance criteria for occupational accidents, which may be useful in overcoming the practical difficulties faced by authorities, companies and experts.

An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.