Relationship of Work Engagement and Quality of Work Life with Nurses Performance in Installations of General Regional Hospital Makassar

Performance is the work result in quality and quantity achieved by employees in carrying out their duties in accordance with the responsibilities assigned to them. The role of reliable and professional employees is very helpful in improving organizational performance. This study aims to analyze the relationship between Work Engagement and Quality of Work Life with the Performance of Nurses in Inpatient Services in Makassar City Hospital. This type of research is a quantitative study using an observational study with a cross-sectional study design. Sampling using stratified random sampling so that the sample in this study were nurses in the inpatient installation of Makassar City Hospital, totaling 167 respondents. The results showed that there is a relationship between Work Engagement based on the Vigor dimension, the Dedication dimension, the absorption dimension, and the nurse's performance. There is a relationship between Quality of Work Life and the performance of nurses in Makassar City Hospital. It is recommended that hospital management keep paying attention to work engagement, especially the Vigor dimension in order to increase the morale of nurses. Leaders need to know what their employees need so that employees can work according to organizational expectations, one of which is by providing motivation. Implementing a culture of health in the work environment so as to create a safe working atmosphere, developing career path plans for nurses, and internalizing the values of good work culture to maintain a sense of pride in the institution.

Ontology Based Semantic Concurrent Activity Recognition

Activity recognition has a vital role in smart home operations. Major challenges in activity recognition are personalization, recognising parallel and interleave activities, erratic degree of dissimilar activities, identification of same object used in multiple activities, catering sensor noise caused by mal-interactions, dynamically determining the context of personalized activities and evolution of generic activity model for new activities. Moreover, object-sensor-based activity recognition by learning for complete activity pattern derived from a generic activity model in sequential and parallel activities may also be asserted as open research realms. A dynamic and generic framework named Ontology driven Semantic Activity Recognition (OSCAR) has been proposed to address the asserted challenges through hybrid of data driven techniques, temporal formalism and knowledge-driven techniques. An unlabelled sensor stream generated by inhabitant’s interactions has been accumulated into sensor repositories that is processed by OSCAR to recognise personalized activities performed in sequential or interleaved fashion. The major modules of OSCAR for activity recognition are sensor properties sequencer, semantic segmentor, personalized activity learner, spurious filter model and ontology evolution model. The spurious semantic segmentation produced by sensor noise or erratic behaviour is removed by Allen’s temporal formalism. Moreover, Tversky’s feature-based similarity has been used to remove the highly similar spurious activities produced as a result of mistaken interactions with wrong home objects. A comprehensive set of experiments has been carried out for evaluating the effectiveness of OSCAR over different metrics such as chi-square distribution, precision, recall and f-measure. In order to measure the performance of proposed technique covering all the possible actions/activities. A standard dataset, named CASAS, has been used for making a comparative analysis of different scenarios in activity recognition with state of the art work by Riboni and KCAR. In order to validate distinct research perspectives such as sensor noise, learning user specific actions; no dataset could comprehend these scenarios to the best of our knowledge. So, a dataset named Data Acquisition Methodology for Smart Homes (DAMSH) was developed while adhering to standard guidelines. The evaluation using stated metrics, over different datasets and comparative analysis with prevalent techniques assert OSCAR as a viable and superior solution. The efficacy of OSCAR is complemented by the distinctive features of dynamically learning personalized actions of inhabitants, boundary detection of activities, ontologies, identification and elimination spurious actions and seed knowledge evolution through ontologies.