Background and Objectives: Early and Enhanced Clinical Exposure immediately places postgraduate students in a clinical setting and incorporates continual hands-on instruction throughout their studies. It aims to motiv...Background and Objectives: Early and Enhanced Clinical Exposure immediately places postgraduate students in a clinical setting and incorporates continual hands-on instruction throughout their studies. It aims to motivate students by strengthening their academics, improving clinical and communication skills, and increasing their confidence. The underlying principles are to provide a clinical context and to ensure that the patient remains the centre of learning. The School of Nursing Sciences implemented this model in 2021 to produce hands-on Masters-level neonatal practitioners who can work in multidisciplinary clinical contexts. Therefore, this study explored the experiences of postgraduate nursing students on the Early and Enhanced Clinical Exposure model and draw implications for the future. Methods: A phenomenological study design was utilized at the University of Zambia, School of Nursing Sciences and comprised of eight Master of Science Neonatal Nursing students in their second year. Convenience sampling was used to select the study site and participants. Data was collected between 15<sup>th</sup> January 2023 and 31<sup>st</sup> January 2023 using an in-depth interview guide. Audio recording and notes were transcribed immediately after data collection. Data analysis was conducted using thematic analysis and codes and themes were constructed from the coded data. Ethical clearance and permission were sought before conducting the study. Results: Four major themes emerged from the study: identity and role confusion, challenging and hectic experiences, positive educational experiences, and personal and professional growth. These themes contributed to the promotion of evidence-based practice by helping students to assess, diagnose, and treat various conditions, as well as gain interest, experience, knowledge, and exposure. Conclusion: The model has a significant impact on motivation to learn, as evidenced by reported increased skill level with potential for use in clinical practice. It is recommended that it be implemented in all postgraduate programs for full-time students.展开更多
Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It...Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It is also playing an essential role in devolving human-robot interaction.The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping.Deep learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current research.This article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense environment.In this article,some classic techniques concerning the existing machine learning were reviewed.And provides deep learning models,a detail of benchmark datasets with their respective domains.This paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and cons.Finally,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular frame.Especially,how to improve the context of video description by analyzing key frames detection through morphological image analysis.Additionally,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining results.The ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various keyframes.These keyframes identify dense events occurring in the video sequence.展开更多
文摘Background and Objectives: Early and Enhanced Clinical Exposure immediately places postgraduate students in a clinical setting and incorporates continual hands-on instruction throughout their studies. It aims to motivate students by strengthening their academics, improving clinical and communication skills, and increasing their confidence. The underlying principles are to provide a clinical context and to ensure that the patient remains the centre of learning. The School of Nursing Sciences implemented this model in 2021 to produce hands-on Masters-level neonatal practitioners who can work in multidisciplinary clinical contexts. Therefore, this study explored the experiences of postgraduate nursing students on the Early and Enhanced Clinical Exposure model and draw implications for the future. Methods: A phenomenological study design was utilized at the University of Zambia, School of Nursing Sciences and comprised of eight Master of Science Neonatal Nursing students in their second year. Convenience sampling was used to select the study site and participants. Data was collected between 15<sup>th</sup> January 2023 and 31<sup>st</sup> January 2023 using an in-depth interview guide. Audio recording and notes were transcribed immediately after data collection. Data analysis was conducted using thematic analysis and codes and themes were constructed from the coded data. Ethical clearance and permission were sought before conducting the study. Results: Four major themes emerged from the study: identity and role confusion, challenging and hectic experiences, positive educational experiences, and personal and professional growth. These themes contributed to the promotion of evidence-based practice by helping students to assess, diagnose, and treat various conditions, as well as gain interest, experience, knowledge, and exposure. Conclusion: The model has a significant impact on motivation to learn, as evidenced by reported increased skill level with potential for use in clinical practice. It is recommended that it be implemented in all postgraduate programs for full-time students.
文摘Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It is also playing an essential role in devolving human-robot interaction.The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping.Deep learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current research.This article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense environment.In this article,some classic techniques concerning the existing machine learning were reviewed.And provides deep learning models,a detail of benchmark datasets with their respective domains.This paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and cons.Finally,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular frame.Especially,how to improve the context of video description by analyzing key frames detection through morphological image analysis.Additionally,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining results.The ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various keyframes.These keyframes identify dense events occurring in the video sequence.
