Hybrid Training Research Articles

Implementation and Assessment of a Hybrid Training Course on Point-of-Care Pediatric Ultrasound in Vietnam During the COVID-19 Pandemic.

Background As point-of-care ultrasound (POCUS) has gained popularity, some educational guidelines have been developed. However, in Vietnam, no training course in pediatric POCUS has yet been developed. This was challenging, especially during the COVID-19 pandemic. Objectives This study aimed to implement a three-month hybridtraining course for pediatric POCUS training in Vietnam using both online and face-to-face hands-on sessions and to assess participants' self-efficacy level and change in their attitudes towards pediatric POCUS. Methods A hybrid training course in pediatric POCUS was implemented at a children's hospital in Vietnam. This study developed a standardized training course, including online learning, live lectures, hands-on sessions, and skill assessment based on the POCUS consensus educational guidelines. Physicians interested in pediatric POCUS were recruited for participation. They completed a self-evaluation survey before and after the course using a Likert score to assess their background, self-efficacy in performing POCUS, overall satisfaction with the course, and change in their attitudes towards POCUS three months after the course. Results A total of 19 physicians participated in the course. The mean post-training self-efficacy score was significantly higher than the pre-course assessment score: 73.1 (standard deviation (SD): 7.2) vs. 48.9 (SD: 12.5) (p <0.05). The efficacy level was retained three months after the course. Furthermore, overall satisfaction with the course was high at 9.5 (SD: 0.6). After the course, almost all participants strongly agreed to increase the use of POCUS in their clinical practice. Conclusion A hybrid training course in pediatric POCUS was successfully implemented in Vietnam and found the participants' self-efficacy level to be significantly higher after the course and the effect to be retained after the course. The training course could positively affect the participants' attitudes towards POCUS, encouraging them to use POCUS more frequently in their clinical practice.

A virtual simulation-based training program on birthing positions: a randomized controlled trial

BackgroundRestricting parturient women in healthcare facilities from choosing positions that provide the greatest comfort and benefit during labor is a global barrier. Several complex factors, including caregiver preference and medical intervention, shape the limitation. Therefore, a practical need exists to train midwives on the knowledge and skills to change this condition.MethodsThe study used a parallel, single-blind, randomized controlled trial at a provincial maternity and child health hospital in Fujian, China, from June 1 to December 31, 2019. The midwives in a birth suite were selected and randomly enrolled in a one-month simulation-based hybrid training or face-to-face teaching in September 2019. The four-level Kirkpatrick’s model, including reaction, learning, behavior, and results, was used to evaluate training effects before and after the program. Data were analyzed with SPSS 25.0 using Student’s t-test, Spearman’s correlation test, Mann–Whitney U test, Wilcoxon signed-rank test, and chi-square test analysis of variance. The significance level was set at p < 0.05.ResultsForty-two midwives were initially randomized to either the virtual simulation group or the face-to-face group. One midwife was excluded from the analysis due to intervention discontinuation, resulting in a final analysis of 41 midwives (n1 = 21, n2 = 20). Post-intervention, the virtual simulation group exhibited higher satisfaction and learning effects compared to the face-to-face group, while the rate of perineal incision in primiparas was lower (p<0.05). No significant changes or differences were observed in self-rated behavior between the two groups (p>0.05). The virtual simulation group demonstrated an increase in non-supine birth rate (p = 0.030) and a decrease in perineal incision rate among primiparas compared to pre-intervention (p = 0.035). Moreover, knowledge performance was associated with the duration of virtual simulation (r = 0.664, p = 0.001).ConclusionsVirtual simulation is a fascinating innovation that enables midwives to develop birthing positions without practicing on real pregnant women and is one solution to achieve work competency within a shortened training period.

INFLUENCE OF HYBRID EDUCATION TRENDS ON THE TEACHING METHODS OF MEDICAL STUDENTS

The article discusses innovative learning models, as well as trends in the development of hybrid education, including alternative forms of education, and its impact on the teaching methods of medical university students.&#x0D; The aim of the study was to study the formation of the content of education in conditions of simultaneous combination of digital and traditional teaching methods. An analysis of the works of both Russian and European authors has shown that some identify mixed and hybrid education; others distinguish between these concepts, while mixed learning consists of online (distance) and offline (traditional) forms. Whereas hybrid learning combines various forms of learning: informal and independent education, tutoring.&#x0D; In this work, a mixed methodology was used, as well as interdisciplinary and interpretative analysis.&#x0D; The conducted research allowed the author to conclude that hybrid training is designed to solve a number of tasks that are being put forward today in the medical education system: increasing the availability of information, taking into account the personality-oriented educational needs of students. At the same time, the importance of the thoughtfulness of the hybrid educational process is emphasized, otherwise, with the inept design and use of hybrid learning, it is possible to create uncomfortable conditions for all participants in the educational process.

Assessing the Impact of the COVID-19 Pandemic on Training at the MD Anderson Cancer Center Anatomical Pathology Fellowship Program.

To provide high-quality, safe training during the COVID-19 pandemic, our anatomic pathology fellowship program implemented a hybrid virtual/in-person training model with supplemental digital material. To evaluate the impact of this model. We examined Accreditation Council for Graduate Medical Education survey results and board pass rates for fellows before the pandemic (group 1); during the pandemic peak (group 2); and early and late after the pandemic peak (groups 3 and 4). Additionally, we distributed an online survey, including questions related to performance as attending physicians and fellowship experience, to recent graduates. Information loss during handover, supervision and teaching by faculty, and having at least 4 free days a month exhibited the greatest score declines between group 1 and groups 2, 3, and 4 on the Accreditation Council for Graduate Medical Education surveys. No differences were seen in board passing rates between groups. The groups did not differ in responses regarding preparation for role as attending, confidence in role as attending, or overall impression of the fellowship program. The pandemic-affected groups responded more positively on the perceived utility of supplemental digital material, impact of digital pathology on quality of education, and impact of supplemental digital material on familiarity with digital pathology. The difference was particularly large between group 1 and combined groups 3 and 4. Despite the limitations noted, the hybrid training model was effective and successfully prepared fellows for their role as attending physicians. Similar studies can be informative for the implementation of similar programs or for the meaningful integration of digital pathology into training curricula.

The intercultural communication competence improvement for pre-service CSL teachers: A blended learning method based on SVVR

BACKGROUND: With the growing global influence of new coronary pneumonia and an increasing number of successful education case, spherical video-based virtual reality (SVVR) has arisen as an application for effective integration with teaching and learning. OBJECTIVE: This is a mixed-methods study on SVVR-based training for pre-service CSL teachers to more effectively develop their cross-cultural communication and interaction abilities. METHODS: An experimental strategy was adopted in this work. First, the processing of VR technology for designed TVET courses, including practical training courses, had been completed; The second experiment was a group experiment of one month; Finally, the hypotheses were tested by an independent sample t-test. RESULTS: Based on experiment data of the learners, this study build a multi-modal hybrid training and recognition model and confirm hybrid approach effectively improves the intercultural communication abilities of pre-service CSL teachers. CONCLUSIONS: The findings of this study confirm that information technology plays an important role in pre-service CSL teacher training, and teachers should incorporate more information technology, design more hands-on interactive activities and scenarios, and take into account the individual differences of learners in the teaching process. Several policy proposals are offered to ensure that ICT is better integrated into classroom instruction.

Training paediatric hospital workforces to deliver low-intensity CBT for children and young people with mental health needs in the context of long term conditions

IntroductionDespite children and young people (CYP) with long term conditions (LTCs) having significantly elevated mental health needs, accessing evidence-based psychological support remains difficult. Previous work suggests low-intensity CBT (LICBT) interventions are effective for CYP with mental health needs in the context of LTCs. If embedded in hospitals LICBT may increase access and better integrate physical and mental health care. This new provision is currently being implemented in paediatric hospitals across the UK. A vital part of successful implementation is effective training. The current paper describes hybrid training in LICBT interventions delivered to staff from a range of health care settings. The aim of the study was to analyse the effectiveness of, and satisfaction with, this training.MethodsTwo days of hybrid training in LICBT for mental health needs in the context of LTCs were delivered to staff. Attendees were introduced to LICBT strategies to support CYP with anxiety, depression and challenging behaviour. A brief questionnaire and evaluation form were completed before and after training on attendees’ understanding of the topics covered and their views of the training (usefulness, relevance, preparedness).ResultsThirteen attendees completed the questionnaire before and after training was completed. Total scores significantly increased from pre-training to post-training. Qualitative feedback indicated the majority found the training highly relevant to their work and enjoyed the hybrid model.ConclusionThese findings support the acceptability and effectiveness of two-day hybrid training as part of enabling a paediatric hospital workforce to deliver psychological support for CYP with LTCs. Future training could consider this format to help overcome barriers to successful implementation. This work highlights the opportunity a new or existing workforce holds in integrating psychological therapies into existing physical health care pathways, coordinating care and ensuring equitable access to mental health support for CYPs, irrespective of their LTCs.

Underwater object tracker: UOSTrack for marine organism grasping of underwater vehicles

A visual single-object tracker is an indispensable component of underwater vehicles (UVs) in marine organism grasping tasks. Its accuracy and stability are imperative to guide the UVs to perform grasping behavior. Although single-object trackers show competitive performance in the challenge of underwater image degradation, there are still issues with sample imbalance and exclusion of similar objects that need to be addressed for application in marine organism grasping. This paper proposes Underwater OSTrack (UOSTrack), which consists of underwater image and open-air sequence hybrid training (UOHT), and motion-based post-processing (MBPP). The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions. The MBPP paradigm is proposed to exclude similar objects. It uses the estimation box predicted with a Kalman filter and the candidate boxes in the response map to relocate the lost tracked object in the candidate area. UOSTrack achieves an average performance improvement of 4.41% and 7.98% maximum compared to state-of-the-art methods on various benchmarks, respectively. Field experiments have verified the accuracy and stability of our proposed UOSTrack for UVs in marine organism grasping tasks. More details can be found at https://github.com/LiYunfengLYF/UOSTrack.

Practicing Emergency Medicine in the Metaverse: A Novel Mixed Reality Casualty Care Training Platform.

Current casualty care training modalities present several challenges, including limited simulation facilities, instructor dependence, lack of standardization, documentation of trainees' performance and training personalization. The study presents the design, development and preliminary evaluation of a novel hybrid training platform to address these challenges. A mixed reality platform was chosen and developed to address field operators' requirements. The platform is easy to operate and can be set up by laypeople within 20-min in multiple environments. Individual-level training documentation is generated autonomously following each session, evaluating 30 aspects of performance. From this, a unique aggregated dataset emerges as a substrate for executives' dashboards and intelligent planning of future sessions. An evaluation process took part using simulator-based training in different stages along the project using a questionnaire (Likert-scale based). Fifty military physicians took part in an identical head injury scenario requiring airway management by endotracheal intubation and were immediately surveyed. TrauMR is an agile hybrid training that harbors the potential to address many of the emerging challenges of training for prehospital care in combat and civilian environments.

MFERMeta++ : Robust Multiview Facial Expression Recognition Based on Metahuman and Metalearning

Facial pose variation presents a significant challenge to facial expression recognition (FER) in real‐world applications. Significant bottlenecks exist in the field of multiview facial expression recognition (MFER) including a lack of high‐quality MFER datasets, and the limited model robustness in real‐world MFER scenarios. Therefore, this article first introduces a metahuman‐based MFER dataset (MMED), which effectively addresses the issues of insufficient quantity and quality in existing datasets. Second, a conditional cascade VGG (ccVGG) model is proposed, which can adaptively adjust expression feature extraction based on the input image's pose information. Finally, a hybrid training and few‐shot learning strategy are proposed that integrates our MMED dataset with a real‐world dataset and quickly deploys it in real‐world application scenarios using the proposed Meta‐Dist few‐shot learning method. Experiments on the Karolinska Directed Emotional Face (KDEF) dataset demonstrate that the proposed model exhibits improved robustness in multiview application scenarios and achieves a recognition accuracy improvement of 28.68% relative to the baseline. It demonstrates that the proposed MMED dataset can effectively improve the training efficiency of MFER models and facilitate easy deployment in real‐world applications. This work provides a reliable dataset for the MFER studies and paves the way for robust FER in any view for real‐world deployment.

A Transformer-Based Framework for Parameter Learning of a Land Surface Hydrological Process Model

The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a hydrological parameter calibration training framework consisting of a transformer-based parameter learning model (ParaFormer) and a surrogate model based on LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms to learn a global mapping from observed data to the parameters to be calibrated, which captures spatial correlations. On the other hand, the surrogate model takes the calibrated parameters as inputs and simulates the observable variables, such as soil moisture, overcoming the challenges of directly combining complex hydrological models with a deep learning (DL) platform in a hybrid training scheme. Using the variable infiltration capacity model as the reference, we test the performance of ParaFormer on datasets of different resolutions. The results demonstrate that, in predicting soil moisture and transferring calibrated parameters in the task of evapotranspiration prediction, ParaFormer learns more effective and robust parameter mapping patterns compared to traditional and state-of-the-art DL-based parameter calibration methods.

The STOP Program: a Hybrid Smoking Prevention and Cessation Training for Cancer Care Providers in Colombia and Peru

We designed and tested the feasibility of the Smoking Cessation Training Program for Oncology Practice (STOP), a hybrid (face-to-face plus web-based) educational intervention to enhance Spanish-speaking cancer care professionals’ (CCPs’) ability to provide brief smoking prevention and cessation counseling to cancer patients and survivors. Changes in the CCPs’ competencies (knowledge, attitude, self-efficacy, and practices toward smoking and smoking cessation services) were assessed post-training. Sixty CCPs from one major cancer center in Colombia (n = 30) and Peru (n = 30) were invited to participate in a 4-module hybrid training program on smoking prevention and cessation. Demographic and pre- and post-test evaluation data were collected. The training’s acceptability was measured after each module. Bivariate analysis was conducted using Wilcoxon signed-rank test to compare the CCPs’ competencies before and after the delivery of the STOP Program. Effect sizes were computed over time to assess the sustainability of the acquired competencies. Twenty-nine CCPs in Colombia and 24 CCPs in Peru completed the STOP Program (96.6% and 80.0% retention rates, respectively). In both countries, 98.2% of the CCPs reported that the overall structure and organization of the program provided an excellent learning experience. The pre-post-test evaluations indicated that the CCPs significantly improved their knowledge, attitude, self-efficacy, and practices toward smoking, smoking prevention, and cessation services. We found that the CCPs’ self-efficacy and practices increased over time (1-, 3-, and 6-month assessments after completing the 4 educational modules). The STOP Program was effective and well-received, demonstrating remarkable changes in CCPs’ competencies in providing smoking prevention and cessation services to cancer patients.

A hybrid training algorithm based on gradient descent and evolutionary computation

A hybrid training algorithm based on gradient descent and evolutionary computation

High-definition image transmission through dynamically perturbed multimode fiber by a self-attention based neural network.

We implement faithful multimode fiber (MMF) image transmission by a self-attention-based neural network. Compared with a real-valued artificial neural network (ANN) based on a convolutional neural network (CNN), our method utilizes a self-attention mechanism to achieve a higher image quality. The enhancement measure (EME) and structural similarity (SSIM) of the dataset collected in the experiment improved by 0.79 and 0.04; the total number of parameters can be reduced by up to 25%. To enhance the robustness of the neural network to MMF bending in image transmission, we use a simulation dataset to prove that the hybrid training method is helpful in MMF transmission of a high-definition image. Our findings may pave the way for simpler and more robust single-MMF image transmission schemes with hybrid training; SSIM on datasets under different disturbances improve by 0.18. This system has the potential to be applied to various high-demand image transmission tasks, such as endoscopy.

A syntactic method for robot imitation learning of complex sequence task

PurposeAiming at the problems of weak generalization of robot imitation learning methods and higher accuracy requirements of low-level detectors, this study aims to propose an imitation learning method based on structural grammar.Design/methodology/approachThe paper proposes a hybrid training model based on artificial immune algorithm and the Baum–Welch algorithm to extract the action information of the demonstration activity to form the {action-object} sequence and extract the symbol description of the scene to form the symbol primitives sequence. Then, probabilistic context-free grammar is used to characterize and manipulate these sequences to form a grammar space. Minimum description length criteria are used to evaluate the quality of the grammar in the grammar space, and the improved beam search algorithm is used to find the optimal grammar.FindingsIt is found that the obtained general structure can parse the symbol primitive sequence containing noise and obtain the correct sequence, thereby guiding the robot to perform more complex and higher-order demonstration tasks.Practical implicationsUsing this strategy, the robot completes the fourth-order Hanoi tower task has been verified.Originality/valueAn imitation learning method for robots based on structural grammar is first proposed. The experimental results show that the method has strong generalization ability and good anti-interference performance.

Enhancing Classroom Learning through Hybrid Teaching and Learning: Insights from a Four-Session Training Program

Hybrid learning, with a focus on facilitating students’ problem-solving skills, is a new and innovative approach which teachers can employ to inculcate higher-level thinking among their students. Accordingly, the purpose of this hybrid training program is to equip teachers with the skills to create hybrid learning instruments that facilitate problem-solving in their classrooms. The training methods include presentations, Q&amp;A sessions, discussions and performances in the form of direct exercises guided by a facilitator (or resource person). Offline teachers from Madrasah Ibtidaiyah (MI) Muhammadiyah Kramat and online teachers from a primary school, or Sekolah Dasar (SD) MI Muhammadiyah Se-Banyumas attended the training. In general, training activities are structured into 4 stages, namely (1) training on creating instruments and implementing hybrid learning to support problem-solving; (2) assisting in the creation of hybrid learning instruments; 3) monitoring, testing, and implementing hybrid learning in the classroom; and 4) reflecting on the outcomes of implementing hybrid learning. Insights gained from the training program that aims to create hybrid learning instruments, are presented and discussed.

Accessibility of Participation in a Pollinator-Focused Community Science Project

Ecological research, education, and community engagement were interrupted globally in the spring and summer of 2020 because of the COVID-19 pandemic. To allow for continued data collection and to provide opportunities for people to interact with nature, we developed a community science project focusing on pollinator visitation to gardens and lawns. To evaluate the accessibility of this project to volunteers, we conducted 28 semi-structured interviews with people who participated in the project training. Interviewees experienced a number of barriers to participation, the most common of which were difficulty with the data collection procedure (n = 22), challenges using technology (n = 11), and lack of access to technology (n = 9). However, components of the online training and data collection procedure helped overcome some of these barriers. Strategies such as using a hybrid training format, simplifying the use of technology during training, and incorporating active learning into online workshops could increase participation in community science projects, both in the context of the pandemic and moving forward.

Qualitative study on experience of healthcare staff who have undergone a hybrid root cause analysis training programme

BackgroundRoot cause analysis (RCA) is a structured investigation methodology aimed at identifying systems factors to prevent recurrence of incidents. To enhance staff’s knowledge and skills, a hybrid RCA training course...

What We Talk About When We Talk About Coping: A Qualitative Study of Surgery Resident's Coping After Complications and Deaths.

To explore how surgery residents cope with unwanted patient outcomes including postoperative complications and death. Surgery residents face a variety of work-related stressors that require them to engage in coping strategies. Postoperative complications and deaths are common sources of such stressors. Although few studies examine the response to these events and their impacts on subsequent decision-making, there has been little scholarly work exploring coping strategies among surgery residents specifically. This study investigated the ways, in which general surgery residents cope with unwanted patient outcomes, including complications and deaths. Mid-level and senior residents (n = 28) from 14 academic, community, and hybrid training programs across the United States participated in exploratory semistructured interviews conducted by an experienced anthropologist. Interview transcripts were analyzed iteratively, informed by thematic analysis. When discussing how they cope with complications and deaths, residents described both internal and external strategies. Internal strategies included a sense of inevitability, compartmentalization of emotions or experiences, thoughts of forgiveness, and beliefs surrounding resilience. External strategies included support from colleagues and mentors, commitment to change, and personal practices or rituals, such as exercise or psychotherapy. In this novel qualitative study, general surgery residents described the coping strategies that they organically used after postoperative complications and deaths. To improve resident well-being, it is critical to first understand the natural coping processes. Such efforts will facilitate structuring future support systems to aid residents during these difficult periods.

Organization of training of specialists in the workplace in the format of hybrid learning

The article presents the results of the study, which indicate the real possibility of training specialists on the job. The introduction of a system of continuous professional development in the format of hybrid training creates conditions for consolidating and improving the professional knowledge of nursing staff, which in the future will significantly influence the quality of medical care for the children's population of the region. The developed model of training nurses in a medical organization using a structured program and hybrid training can be used as a tool to consolidate professional knowledge and skills, as well as to form new competencies. Purpose of the study. To increase the professional competence of nursing professionals in caring for children with epilepsy in specialized neurological departments on the basis of organizational modeling of part-time nursing staff training courses conducted in a medical organization. Results. After training during reflection, the nurses expressed the following opinions: «There was a desire to engage in repeated training cycles»; «The attitude to the performance of professional duties has changed»; «Responsibility for the result of care has increased»; «Working with cases contributed to the formation of clinical thinking and stimulated the search for additional information to improve one’s competence», 54.3 ± 4.9; 96.7 ± 1.84; 53.2 ± 6.2; 67.4 ± 4.2 per 100 respondents, respectively. All nurses (100 %) noted that the new form of final certification not only interested them, but also prompted a desire to take part in the WORLDskills. championship. сonclusion. The study is of practical importance for specialists in medical and educational organizations for organizing a hybrid form of education.

An instance-based deep transfer learning method for quality identification of Longjing tea from multiple geographical origins

For practitioners, it is very crucial to realize accurate and automatic vision-based quality identification of Longjing tea. Due to the high similarity between classes, the classification accuracy of traditional image processing combined with machine learning algorithm is not satisfactory. High-performance deep learning methods require large amounts of annotated data, but collecting and labeling massive amounts of data is very time consuming and monotonous. To gain as much useful knowledge as possible from related tasks, an instance-based deep transfer learning method for the quality identification of Longjing tea is proposed. The method mainly consists of two steps: (i) The MobileNet V2 model is trained using the hybrid training dataset containing all labeled samples from source and target domains. The trained MobileNet V2 model is used as a feature extractor, and (ii) the extracted features are input into the proposed multiclass TrAdaBoost algorithm for training and identification. Longjing tea images from three geographical origins, West Lake, Qiantang, and Yuezhou, are collected, and the tea from each geographical origin contains four grades. The Longjing tea from West Lake is regarded as the source domain, which contains more labeled samples. The Longjing tea from the other two geographical origins contains only limited labeled samples, which are regarded as the target domain. Comparative experimental results show that the method with the best performance is the MobileNet V2 feature extractor trained with a hybrid training dataset combined with multiclass TrAdaBoost with linear support vector machine (SVM). The overall Longjing tea quality identification accuracy is 93.6% and 91.5% on the two target domain datasets, respectively. The proposed method can achieve accurate quality identification of Longjing tea with limited samples. It can provide some heuristics for designing image-based tea quality identification systems.