Determining tactics that influence partners in the creation of an interagency information sharing system

Partnership working is increasing dramatically in the public sector in the UK. Partly as a result of reduced funding (as part of government budget cuts) but also as a reaction to the increasing realisation that sharing information improves the service individual partners can provide. This brings a new paradigm to sharing information for non-competitive purposes. To achieve the partnership aim of providing a better service each partner must attempt to put aside their normal ways of working (i.e. protecting their information) and attempt to produce an information sharing system where information can be shared legally, purposefully and in a timely manner. This paradigm is in most cases new to the organisations involved and their approach to the level of influence they have in a partnership to produce a system can be challenging. This paper forms part of a larger research project, researching how public sector agencies can share information more effectively. The goal of the research is to develop a model for partnership information sharing, which models the outcomes of decisions made during the development stages of the system and how these have affected the overall acceptance and success of the system. The paper provides a classification of encouragement tactics which partners in a public sector partnership can utilise when implementing a new information sharing system to achieve their own objectives. The encouragement tactics classification helps to both clarify the concepts of power and influence by providing a clear distinction between the terms and bridge the terms by combining them in a single classification. This approach of a unifying classification has not previously been attempted and further work is required to validate the classification proposed in this paper. The classification has been created from participant observation of the creation of a trailblazing information sharing system between the police and councils (districts, county and city) to improve their ability to handle antisocial behaviour

What about mood swings? Identifying depression on Twitter with temporal measures of emotions

Depression is among the most commonly diagnosed mental disorders around the world. With the increasing popularity of online social network platforms and the advances in data science, more research efforts have been spent on understanding mental disorders through social media by analysing linguistic style, sentiment, online social networks and other activity traces. However, the role of basic emotions and their changes over time, have not yet been fully explored in extant work. In this paper, we proposed a novel approach for identifying users with or at risk of depression by incorporating measures of eight basic emotions as features from Twitter posts over time, including a temporal analysis of these features. The results showed that emotion-related expressions can reveal insights of individuals’ psychological states and emotions measured from such expressions show predictive power of identifying depression on Twitter. We also demonstrated that the changes in an individual’s emotions as measured over time bear additional information and can further improve the effectiveness of emotions as features, hence, improve the performance of our proposed model in this task

Has research finally uncovered how to conquer your work-email?

No description supplied</p

Assessing the value of an E-mail knowledge extraction system

This paper reviews an approach to locating knowledge holders within organizations through the use of a well-established communication medium, E-mail. The approach has been used to develop the E-mail knowledge extraction (EKE) tool. EKE was then evaluated at an academic institution in the United Kingdom. This study represents the first effort to validate the viability of the E-mail medium as a source of knowledge profiling data, to be used for finding employees who possess the required knowledge. It also looks at the socio–ethical challenges associated with EKE’s adoption. The overall evaluation of EKE found it to be useful, interesting, easy and intuitive to use and of potential benefit to employees within organizations

Classifying computer-mediated communication (CMC) interruptions at work using control as a key delineator

Computer-mediated communication (CMC) interruptions are a common feature of people’s work activity. In studying interruptions, researchers can understand how people manage and co-ordinate their work when faced with multiple, often competing, demands. However, CMC interruptions are characteristically different from each other and impact people’s work performance in different ways. In this theoretical paper we define and differentiate between computer-mediated communication (CMC) interruptions, according to the level of control people are able to exert over an interruption at different points in the delivery timeline. Informed by the extant interruptions literature and Action Regulation Theory, a classification framework is provided, to help researchers and work designers distinguish which types of real-world CMC interruption are more or less disruptive, based on levels of control. Using the developed framework, two key research propositions are made, which we encourage future research to attend to. Unique contributions and implications of this paper are discussed

Additional file 1: Figure S1. of Delirium is not associated with anticholinergic burden or polypharmacy in older patients on admission to an acute hospital: an observational case control study

Flowchart of selection of study participants. Flowchart demonstrating selection of participants to the delirium and no-delirium groups and the number of patients recruited. The number of patients excluded and not recruited are also displayed and the reason they were not included. (DOCX 30 kb

In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles

Developing conditionally automated driving systems is on the rise. Vehicles with full longitudinal and latitudinal control will allow drivers to engage in secondary tasks without monitoring the roadway, but users may be required to resume vehicle control to handle critical hazards. The loss of driver’s situational awareness increases the potential for accidents. Thus, the automated systems need to estimate the driver’s ability to resume control of the driving task. The aim of this study was to assess the physiological behaviour (heart rate and pupil diameter) of drivers. The assessment was performed during two naturalistic secondary tasks. The tasks were the email and the twenty questions task in addition to a control group that did not perform any tasks. The study aimed at finding possible correlations between the driver’s physiological data and their responses to a takeover request. A driving simulator study was used to collect data from a total of 33 participants in a repeated measures design to examine the physiological changes during driving and to measure their takeover quality and response time. Secondary tasks induced changes on physiological measures and a small influence on response time. However, there was a strong observed correlation between the physiological measures and response time. Takeover quality in this study was assessed using two new performance measures called PerSpeed and PerAngle. They are identified as the mean percentage change of vehicle’s speed and heading angle starting from a take-over request time. Using linear mixed models, there was a strong interaction between task, heart rate and pupil diameter and PerSpeed, PerAngle and response time. This, in turn, provided a measurable understanding of a driver’s future responses to the automated system based on the driver’s physiological changes to allow better decision making. The present findings of this study emphasised the possibility of building a driver mental state model and prediction system to determine the quality of the driver's responses in a highly automated vehicle. Such results will reduce accidents and enhance the driver’s experience in highly automated vehicles

Mobile access to information systems in law enforcement: an evaluation of its implications for data quality [conference paper]

A recent UK government initiative enables police officers to input information directly into policing information systems via mobile devices. However, the impact and implications on data quality have not been assessed. The events of 9/11 and the Soham murders in the UK in 2002 may reflect high profile incidents of failure in information management practice within police forces that have amplified the need to scrutinise the monitoring of data quality. The tragedy of the Soham murders was partly caused by poor quality information regarding the offender, Ian Huntley, being held on disparate information systems. Consequently, intelligence and information held on people must be fully accurate, and therefore data quality plays a pivotal role. Despite the apparent severe impact of poor data quality on organisational effectiveness and decision-making, previous research appears to have addressed these issues only within non-policing sectors. The paper investigates what measures are used to monitor data quality via an empirical study within a UK police force, the Leicestershire Constabulary. It also evaluates the design of the interface of the crime-input form and the impact this has on inputting quality information into the crime recording system, along with the implications of this for modern-day law enforcement. Measurement of data quality was investigated by mapping aspects of the data quality monitoring process identified via qualitative data from semi-structured interviews against the key attributes of data quality derived from a literature review. The design of the crime-recording interface was evaluated via a series of focus groups with operational users of mobile technology prior to and following implementation of mobile devices. The research found that there are some processes in place to check that data follows specific standards, such as the recording of dates of birth. However, these processes only take into consideration the structural completeness of data, and other measurements of data quality, such as accuracy, timeliness, relevance, understandability and consistency are not considered. It also found that the existing interface is inefficient for a mobile environment, as there are numerous free-text fields and duplication of data entry caused by a lack of system integration. The paper contributes to the existing small body of knowledge on data quality within a mobile policing environment. This knowledge can be applied by other law enforcement organisations looking to provide mobile access to their information and knowledge environment without reducing the level of data quality as a result of direct input of information

Sea surface temperature (SST) results from the study.

<p>(a) Shows the time series of SST acquired by the surfer at Wembury beach overlain onto the daily median SST data from station L4 during the study period (<i>N</i> refers to the number of samples). (b) Shows a scatter plot of daily match-ups between SST acquired by the surfer at Wembury beach and SST data from station L4. Bracketed statistics refer to use of hourly match-ups between the two datasets. (c) Shows the time series of SST from satellite (AVHRR) at station L4 overlain onto the daily median SST data from station L4 (buoy) during the study period. (d) Shows a scatter plot of daily match-ups between SST from satellite (AVHRR) at station L4 and SST data from the buoy at station L4. (e) Shows the time series of SST from satellite (AVHRR) at Wembury beach overlain onto SST acquired by the surfer at Wembury beach during the study period. (f) Shows a scatter plot of daily match-ups between SST from satellite (AVHRR) at Wembury beach and SST data acquired by the surfer at Wembury beach. Statistics are denoted as follows: <i>r</i>² is the squared Pearson correlation coefficient; Ψ is the Root Mean Square Error; Δ is the unbiased Root Mean Square Error; <i>δ</i> is the bias; <i>S</i> and <i>I</i> are the slope and intercept of a linear regression respectively; and <i>N</i> refers to the number of match-ups.</p

Equipment used in the study and surfer set-up.

<p>(a) Shows the Tidbit V2 temperature logger attached at mid-point to the surfboard leash. HOBOware software and HOBO USB Optic Base Station (BASE-U-4) were used by the surfer to launch the Tidbit V2 temperature logger prior to each session, and then to upload data post session. (b) Shows the GARMIN extrex 10 GPS, water-resistant Aquapac and waist-bag worn by the surfer. Information at one second intervals on location (latitude and longitude), time, distance, speed and orientation for each surf, were extracted from the GPS device post session. (c) Shows the surfer equipped with the sensors, and (d) shows the surfer collecting data during a session at Wembury beach. Consent to publication was obtained from the participant in this figure.</p