Search

Background: Poor insight is associated with negative attitudes to involuntary admission and care in qualitative studies. Aims: The current paper aims to examine and compare retrospective qualitative perceptions of service-users in relation to their involuntary admission with their levels of clinical insight, using a mixed methods approach. Methods: Forty two participants were assessed 3 months after the revocation of their involuntary admission. Each provided qualitative data relating to their perceptions of the coercive care process, which was analysed using content analysis, along with a quantitative measurement of insight, the Schedule for the Assessment of Insight-Expanded (SAI-E). Employing a mixed methods design and incorporating NVivo matrix coding queries, the datasets were merged to enable qualitative themes to be identified against the quantitative data. Results: Differences were observed between those with high and low insight in terms of their understanding of the need for treatment, their levels of arousal at the time of admission and how they perceived the compassion of health professionals. Certain negative perceptions of care appeared more universal and were common across those with high and low insight. Conclusion: Some negative perceptions of coercive practices appear linked to inherent elements of psychotic illness such as unawareness of illness. Individuals with higher levels of insight tended to perceive their involuntary admission and receiving a diagnosis as beneficial. Negative views that persist amongst service users with high insight levels can highlight areas for successful service improvement, including increased emphasis on non-pharmacotherapy based supports during the coercive care process.

Electrical utilities are faced with a challenge of processing large amount of data and presenting operators with optimal amount of information, which identifies the essential and critical state of the grid. The data have to be in form, which can be easily consumed by operators and easily associated with functional and geographical knowledge of the grid. Most of the visual presentations in power systems are focused on real-time and historical rendering of information. In modern electrical utility environment, it is becoming important to visualize the information predicted for the future state of the power system. The software development work was performed in a large electrical utility of USA in order to enhance visualization of future power system state. The implemented features encompass advanced future state monitoring, transmission reliability index, dashboard, global system wide alarms, and rendering of the forecasted weather and power system state data on GIS maps.

Complexities of smart grid and increasing share of renewable resources require development of intelligent decision-making support systems. Predictive Analysis of the power system extended several hours into near future can provide an insight into potential operational emergencies and assist in taking proactive corrective actions. The proposed prediction of network conditions takes advantage of load forecast, generation forecast, interchange schedules, dynamic line ratings, planned outages, and weather forecast. This paper presents the implementation results of look-ahead analysis for a large US electrical utility. Monitoring of projected violations and the derivatives of their trends provide enhanced monitoring capability to detect dangerous situations developing in the power system. The real-time prediction and intelligent alarming enhances situational awareness of operators in the control room.