A Paradigm Shift From Brick and Mortar: Full-Time Nursing Faculty Off Campus

Beck, M. Berenice; Bradley, Holly B.; Cook, Linda L.; Leasca, Joslin B.; Lampley, Tammy M.; Gatti-Petito, JoAnne

doi:10.1097/01.nep.0000000000000211

Cited by 5 publications

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“…27 Collaborative learning supports the academic socialization process and facilitates the sense of social presence in the classroom environment. 28,29 For students enrolled in traditional settings, social and academic integration can occur more seamlessly. The goal of the Let's Check-In: Tips for Success approach was to develop a distance learning community, where students would become active participants in their learning process, synonymous with experiences in a traditional classroom.…”

Section: Discussionmentioning

confidence: 99%

“…Identifying additional platforms and technology to enhance engagement between students and faculty allows for the development of the distance learning community 27. Collaborative learning supports the academic socialization process and facilitates the sense of social presence in the classroom environment 28,29. For students enrolled in traditional settings, social and academic integration can occur more seamlessly.…”

Section: Discussionmentioning

confidence: 99%

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Engaging Students and Faculty in a Collaborative Learning Environment

2022

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Background: The increasing move to distance education has exposed gaps in the implementation and sustainability of collaborative learning. Collaborative learning requires a mutual approach to knowledge building, which includes both students and faculty. Purpose: This quality improvement pilot focused on providing additional socialization and collaborative learning in an asynchronous environment. Methods: A review of the literature identified opportunities to address the social and academic needs of students pursuing an online degree, along with strategies to address feelings of isolation experienced by some students in a distance learning environment. The Let's Check-In: Tips for Success webinars were developed and integrated throughout the graduate curriculum. Results: Students provided positive feedback verbally and in end-of-course evaluations. Course faculty reported a more synergistic approach to teaching. Conclusion: Let's Check-In: Tips for Success provided an environment where student communication and understanding of course content increased.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Engaging Students and Faculty in a Collaborative Learning Environment

2022

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“…In recent years, medical research has shown that cerebrovascular events' timing is closely related to the circadian rhythm of human blood pressure [7]. With the birth and development of time medicine and its penetration and promotion of nursing discipline, today's nursing model has been derived, and time nursing has begun to attract scholars' attention [8][9][10][11][12]. So far, many kinds of regression models have been established for continuous measurement of blood pressure, among which pulse wave velocity (PWV) is the most common.…”

Section: Introductionmentioning

confidence: 99%

Blood Pressure Model Based on Hybrid Feature Convolution Neural Network in Promoting Rehabilitation of Patients with Hypertensive Intracerebral Hemorrhage

Zheng

Bai

2021

Computational and Mathematical Methods in Medicine

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Objective. Accurate prediction of the rise of blood pressure is essential for the hypertensive intracerebral hemorrhage. This study uses the hybrid feature convolution neural network to establish the blood pressure model instead of the traditional method of pulse waves. Methods. The pulse waves of 100 patients were collected, and the pulse wave was decomposed into three bell wave compound forms to obtain the accurate pulse wave propagation time. Then, the mixed feature convolution neural network model ABP-net was proposed, which combined the pulse wave propagation time characteristics with the pulse wave waveform characteristics automatically extracted by one-dimensional convolution to predict the arterial blood pressure. Finally, according to the prediction results, 20 patients were treated before the high blood pressure appeared (model group), and another 20 patients with a daily fixed treatment scheme were selected as the control group. Results. In 80 training sets, compared with linear regression and the random forest method, the hybrid feature convolution neural network has higher accuracy in predicting blood pressure. In 20 test sets, the blood pressure error was eliminated within 5 mmHg. The total effective rate in the model group and the control group was 95.0% and 85.0%, respectively ( P = 0.035 ). After treatment, the scores of self-care ability of daily life and limb motor function in the model group were higher than those in the control group ( P < 0.05 ). There were 8 cases (13.6%) in the model group and 17 cases (28.3%) in the control group due to the recurrence of cerebrovascular accident ( P = 0.043 ). Conclusion. Drug treatment guided by a blood pressure model based on a hybrid feature convolution neural network for patients with hypertensive cerebral hemorrhage can significantly and smoothly reduce blood pressure, promote the health recovery, and reduce the occurrence of cerebrovascular accidents.

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