An Updated Rounds Checklist to Increase Appropriate Use of Telemetry Monitoring

Background: Telemetry monitoring is an essential tool to monitor cardiac electrical activity. Its overuse is costly in time and resources and leads to subsequent testing and treatments that are not necessary for the patient and, in addition, healthcare staff is burdened with work that is potentially not clinically useful. Aim: The global aim of increasing efficiency in telemetry use starts with the local improvement to facilitate nurse-physician communication of telemetry patients during Methods: This study with pre and post data collection looked at the results of quantitative data, collected in May-July 2015, on the number of patients with telemetry and the corresponding clinical indication before and after implementation of a modified rounds checklist which included telemetry as a discussion point. The new checklist was initiated on June 22, 2015 and post intervention data was gathered to determine if there was a decrease in the overuse of and increase in the appropriate use of telemetry. Results: With the implementation of the checklist the use of telemetry decreased, however the clinical indication for use did not improve. Conclusion and implications for CNL practice: After the implementation of the checklist criteria there has been a consistent decrease in telemetry use. This may attributable to improve nurse-physician communication, however, there is still a lack of appropriate clinical indication of use and the CNL, as lateral integrator, in future improvement projects, should support further modifications to the clinical indication set to improve appropriateness of telemetry use

Academic achievement : the role of praise in motivating students

The motivation of students is an important issue in higher education, particularly in the context of the increasing diversity of student populations. A social-cognitive perspective assumes motivation to be dynamic, context-sensitive and changeable, thereby rendering it to be a much more differentiated construct than previously understood. This complexity may be perplexing to tutors who are keen to develop applications to improve academic achievement. One application that is within the control of the tutor, at least to some extent, is the use of praise. Using psychological literature the article argues that in motivating students, the tutor is not well served by relying on simplistic and common sense understandings of the construct of praise and that effective applications of praise are mediated by students' goal orientations, which of themselves may be either additive or interactive composites of different objectives and different contexts

Reflexive learning, socio-cognitive conflict and peer-assessment to improve the quality of feedbacks in online tests

International audienceOur previous works have introduced the Tsaap-notes platform dedicated to the semi automatic generation of multiple choice questionnaire providing feedbacks: it reuses interactive questions asked by teachers during lectures, as well as the notes taken by students after the presentation of the results as feedbacks integrated into the quizzes. In this paper, we introduce a new feature which aims at increasing the number of contributions of students in order to significantly improve the quality of the feedbacks used in the resulting quizzes. This feature splits the submission of an answer into several distinct phases to harvest explanations given by students, and then applies an algorithm to filter the best contributions to be integrated as feedbacks in the tests. Our approach has been validated by a first experimentation involving master students enrolled in a computer science course

Reflexive learning, socio-cognitive conflict and peer-assessment to improve the quality of feedbacks in online tests

Our previous works have introduced the Tsaap-notes platform dedicated to the semi automatic generation of multiple choice questionnaire providing feedbacks: it reuses interactive questions asked by teachers during lectures, as well as the notes taken by students after the presentation of the results as feedbacks integrated into the quizzes. In this paper, we introduce a new feature which aims at increasing the number of contributions of students in order to significantly improve the quality of the feedbacks used in the resulting quizzes. This feature splits the submission of an answer into several distinct phases to harvest explanations given by students, and then applies an algorithm to filter the best contributions to be integrated as feedbacks in the tests. Our approach has been validated by a first experimentation involving master students enrolled in a computer science course