Comparing students' performance in self-directed and directed self-learning in College of Medicine, University of Bisha.

Background: Student-centered learning strategy increases the likelihood of graduation of competent, self-dependent, and problem-solving physicians. The University of Bisha, College of Medicine (UBCOM) adopted self-directed learning (SDL) represented by problem-based learning (PBL), and directed self-learning (DSL) represented by team-based learning (TBL). Aim: To compare the students' performance in SDL and DSL among UBCOM students. Methodology: A total of 502 multiple choice questions (MCQs) from the mid-course and final exams were collected by the relevant subject experts from nine courses during the period from September 2020 till June 2023 that adopted PBL and TBL; 247 MCQs related to PBL and 255 related to TBL. Psychometric analysis was used to determine difficult, easy, and optimum questions (≤25%, ≥90%, and 26-89%, respectively). Point biserial as <0.19, 0.20-0.29, 0.30-0.39, and >0.40 which indicate poor, marginal, good, and excellent point biserial, respectively. Finally, the number of functional distractors was attempted by >5% of the candidates. Results: No significant differences were noted for the students' performance in MCQs related to PBL (representing self-directed, small group learning tool), and TBL (representing directed-self, large group learning tool) regarding difficulty index (DI), point biserial, and distractors functionality. Conclusion: It has been observed that there is no difference in students' performance whether PBL or TBL is used for learning Basic Medical Science courses. Small group learning such as PBL needs more resources in comparison to large group learning as in TBL, therefore any institute can decide on the adopted learning strategy depending on its resources and the number of students.

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images.

To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation. To this end, three different datasets were employed used for brain tumor and hippocampus delineation from MR images (BraTS and Decathlon datasets, respectively) and kidney segmentation from CT images (Decathlon dataset). Since data augmentation techniques are also aimed at enhancing the performance of deep learning methods, a deformable data augmentation technique was proposed and compared with contrastive learning and self-learning frameworks. The segmentation accuracy for the three datasets was assessed with and without applying data augmentation, contrastive learning, and self-learning to individually investigate the impact of these techniques. The self-learning and deformable data augmentation techniques exhibited comparable performance with Dice indices of 0.913 ± 0.030 and 0.920 ± 0.022 for kidney segmentation, 0.890 ± 0.035 and 0.898 ± 0.027 for hippocampus segmentation, and 0.891 ± 0.045 and 0.897 ± 0.040 for lesion segmentation, respectively. These two approaches significantly outperformed the contrastive learning and the original model with Dice indices of 0.871 ± 0.039 and 0.868 ± 0.042 for kidney segmentation, 0.872 ± 0.045 and 0.865 ± 0.048 for hippocampus segmentation, and 0.870 ± 0.049 and 0.860 ± 0.058 for lesion segmentation, respectively. The combination of self-learning with deformable data augmentation led to a robust segmentation model with no outliers in the outcomes. This work demonstrated the beneficial impact of self-learning and deformable data augmentation on organ and lesion segmentation, where no additional training datasets are needed.

Ultrasound identification of hand and wrist anatomical structures by hand surgeons new to ultrasonographic techniques.

PURPOSE: Ultrasound is becoming an essential tool for hand surgeons, but most of them are trained on the job, without any diploma or dedicated training. The aim of this study was to assess the ability of hand surgeons new to ultrasound to identify hand and wrist anatomical structures. METHODS: A monocentric study was conducted from January 2022 to April 2022. Ten residents and five attending hand surgeons, ultrasound novices, were involved in this study. The participants underwent two tests, wherein they were required to identify 17 anatomical structures using ultrasound, on the same subject. The second test was similar and carried out 2 to 6 weeks later by all participants. The number of structures successfully identified and if it was the case, the detection time per structure, were recorded. The correlations between participants age, years of surgical experience, surgical background (orthopedic or plastic) and the ability to perform immediately during the first test or to progress between the two tests were also assessed. RESULTS: The average number of structures identified during the first test (T1) was 14.1+/-2.1 (82.9%), versus 16.2+/-0.8 (95.3%) structures during the second test (T2) (p = 0.001). The mean detection time per structure was 53.4 +/- 18.9 s during T1 versus 27.7 +/- 7.2 s during T2 (p < 0.0001). A moderate negative correlation between the progression in the number of anatomical structures identified between the two tests and the years of surgical experience (ρ=-0.56; p = 0.029) was found. The other parameters were neither correlated with the ability to perform at the first test nor with the progression between the two tests. CONCLUSION: Hand surgeons new to ultrasound are most of the time able to identify hand and wrist anatomical structures. Comparison of their first and second tests showed significant potential for improvement in anatomical structure identification and detection time of those, especially in surgeons with limited surgical experience.

Self-directed learning for medical graduates: A boon or bane in disguise? A cross-sectional study in Chennai.

BACKGROUND: Self-directed learning (SDL) is an essential aspect of adult education or andragogy, gaining significance in medical education with the introduction of competency-based medical education. The primary objective of this study is to assess the self-directed learning abilities of second-year medical undergraduates in Chennai, South India, and to identify potential challenges and gaps in their learning process. MATERIALS AND METHODS: A cross-sectional study was conducted among 82 second-year medical students attending self-directed learning sessions at a medical college in Chennai. Data were collected using the self-directed learning instrument (SDLI), a standardized questionnaire, administered through Google Forms. Participants' identities were maintained confidential. Data were analyzed using SPSS version 22.0. Descriptive data were presented as proportions and percentages. Normally distributed quantitative data were expressed as mean and standard deviation. Non-normal continuous data were expressed as median and interquartile range (IQR). RESULTS: The majority of the students (61%) demonstrated a high level of SDL ability, with a median score of 76. Students exhibited strong learning motivation (mean score 4.11) but struggled with planning and implementation (mean score 3.07). The maximum mean score was 4.11 for item 3 (constant improvement and excelling in learning), and the minimum mean score was 3.07 for item 11 (arranging and controlling learning time). The students showed high self-monitoring (mean score 3.76) and interpersonal communication skills (mean score 4.00). CONCLUSIONS: SDL emerges as a boon for medical undergraduates in this study. By providing adequate training to faculty members on SDL implementation and guidance to students on planning and time management, SDL can play a pivotal role in enhancing medical education quality and fostering life-long learning among future medical professionals.

Adaptive dynamic self-learning grey wolf optimization algorithm for solving global optimization problems and engineering problems.

The grey wolf optimization algorithm (GWO) is a new metaheuristic algorithm. The GWO has the advantages of simple structure, few parameters to adjust, and high efficiency, and has been applied in various optimization problems. However, the orginal GWO search process is guided entirely by the best three wolves, resulting in low population diversity, susceptibility to local optima, slow convergence rate, and imbalance in development and exploration. In order to address these shortcomings, this paper proposes an adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO). First, the convergence factor was segmented and nonlinearized to balance the global search and local search of the algorithm and improve the convergence rate. Second, the wolves in the original GWO approach the leader in a straight line, which is too simple and ignores a lot of information on the path. Therefore, a dynamic logarithmic spiral that nonlinearly decreases with the number of iterations was introduced to expand the search range of the algorithm in the early stage and enhance local development in the later stage. Then, the fixed step size in the original GWO can lead to algorithm oscillations and an inability to escape local optima. A dynamic self-learning step size was designed to help the algorithm escape from local optima and prevent oscillations by reasonably learning the current evolution success rate and iteration count. Finally, the original GWO has low population diversity, which makes the algorithm highly susceptible to becoming trapped in local optima. A novel position update strategy was proposed, using the global optimum and randomly generated positions as learning samples, and dynamically controlling the influence of learning samples to increase population diversity and avoid premature convergence of the algorithm. Through comparison with traditional algorithms, such as GWO, PSO, WOA, and the new variant algorithms EOGWO and SOGWO on 23 classical test functions, ASGWO can effectively improve the convergence accuracy and convergence speed, and has a strong ability to escape from local optima. In addition, ASGWO also has good performance in engineering problems (gear train problem, ressure vessel problem, car crashworthiness problem) and feature selection.

Amygdala-derived-EEG-fMRI-pattern neurofeedback for the treatment of chronic post-traumatic stress disorder. A prospective, multicenter, multinational study evaluating clinical efficacy.

We conducted a prospective, single arm, multisite, multinational, open label trial assessing the safety and efficacy of a novel amygdala derived neurofeedback treatment, designated Amygdala-Derived-EFP, for chronic PTSD. Participants, including veterans and civilians, underwent screening, training, 15 neurofeedback sessions over 8 weeks and; baseline, termination (8 weeks) and 3 month post treatment assessments with validated measures. The primary endpoint was more than 50 % of the participants demonstrating a Minimally Clinically Important Difference (MCID) defined as a 6-point reduction, on the Clinician Administered PTSD Scale (CAPS-5) total score at 3 months. Secondary measures included the PCL-5, ERQ, PHQ-9, and CGI. Statistical analyses were performed using SAS®V9.4. The primary endpoint was met, with a CAPS-5 MCID response rate of 66.7 %. The average reduction in CAPS-5 total scores at 3 month follow up was 13.5 points, more than twice the MCID. Changes from baseline in CAPS-5, PCL-5, PHQ-9 scores at 8 weeks and the 3 month follow-up demonstrated statistically significant improvements in response and; demonstrated effect sizes ranging from 0.46 to 1.07. Adverse events were mild and resolved after treatment. This study builds on prior research demonstrating similar outcomes using amygdala-derived neurofeedback. Positive attributes of this therapy include monitoring by non-physician personnel, affordability, accessibility, and tolerability.

An unsupervised two-step training framework for low-dose computed tomography denoising.

BACKGROUND: Although low-dose computed tomography (CT) imaging has been more widely adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT images tend to have more noise, which impedes accurate diagnosis. Recently, deep neural networks using convolutional neural networks to reduce noise in the reconstructed low-dose CT images have shown considerable improvement. However, they need a large number of paired normal- and low-dose CT images to fully train the network via supervised learning methods. PURPOSE: To propose an unsupervised two-step training framework for image denoising that uses low-dose CT images of one dataset and unpaired high-dose CT images from another dataset. METHODS: Our proposed framework trains the denoising network in two steps. In the first training step, we train the network using 3D volumes of CT images and predict the center CT slice from them. This pre-trained network is used in the second training step to train the denoising network and is combined with the memory-efficient denoising generative adversarial network (DenoisingGAN), which further enhances both objective and perceptual quality. RESULTS: The experimental results on phantom and clinical datasets show superior performance over the existing traditional machine learning and self-supervised deep learning methods, and the results are comparable to the fully supervised learning methods. CONCLUSIONS: We proposed a new unsupervised learning framework for low-dose CT denoising, convincingly improving noisy CT images from both objective and perceptual quality perspectives. Because our denoising framework does not require physics-based noise models or system-dependent assumptions, our proposed method can be easily reproduced; consequently, it can also be generally applicable to various CT scanners or dose levels.

Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide.

OBJECTIVES: This study aimed to elucidate effective methodologies for utilizing the generative artificial intelligence (AI) system, namely the Chat Generative Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities among clinicians. METHODS: We conducted a comprehensive exploration of the capabilities of ChatGPT, emphasizing two main areas: (1) efficient utilization of ChatGPT, with a focus on application and language selection, input methodology, and output verification; and (2) specific strategies to bolster clinical reasoning using ChatGPT, including self-learning via simulated clinical case creation and engagement with published case reports. RESULTS: Effective AI-based clinical reasoning development requires a clear delineation of both system roles and user needs. All outputs from the system necessitate rigorous verification against credible medical resources. When used in self-learning scenarios, capabilities of ChatGPT in clinical case creation notably enhanced disease comprehension. CONCLUSIONS: The efficient use of generative AIs, as exemplified by ChatGPT, can impressively enhance clinical reasoning among medical professionals. Adopting these cutting-edge tools promises a bright future for continuous advancements in clinicians' diagnostic skills, heralding a transformative era in digital healthcare.

TestFit: A plug-and-play one-pass test time method for medical image segmentation.

Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning fashion. We propose a new online test-time method, called TestFit, to improve results of a given off-the-shelf DL segmentation model in test time by actively fitting the test data distribution. TestFit first creates a supplementary network (SuppNet) from the given trained off-the-shelf segmentation network (this original network is referred to as OGNet) and applies SuppNet together with OGNet for test time inference. OGNet keeps its hypothesis derived from the original training set to prevent the model from collapsing, while SuppNet seeks to fit the test data distribution. Segmentation results and supervision signals (for updating SuppNet) are generated by combining the outputs of OGNet and SuppNet on the fly. TestFit needs only one pass on each test sample - the same as the traditional test model pipeline - and requires no training time preparation. Since it is challenging to look at only one test sample and no manual annotation for model update each time, we develop a series of technical treatments for improving the stability and effectiveness of our proposed online test-time training method. TestFit works in a plug-and-play fashion, requires minimal hyper-parameter tuning, and is easy to use in practice. Experiments on a large collection of 2D and 3D datasets demonstrate the capability of our TestFit method.

Virtual microscopy as a teaching-learning tool for histology in a competency-based medical curriculum.

Background: Histology forms an important component of first-year medical education. Unfortunately, it is limited to the practical laboratory due to the need for a microscope and good quality slides. Virtual microscopy is a recent advancement, which uses computers as an alternative to microscopes. The aim of the study was to compare virtual microscopy (VM)-based practical classes with traditional microscopy (TM)-based practical classes for two cohorts of first-year medical students, by comparing learning achieved using two different test scores as well as a qualitative assessment of student and faculty perspectives regarding the feasibility and usefulness of VM. Methods: Each cohort of students was divided into two equal batches and each batch underwent eight histology modules of which, four utilised traditional microscopes and four utilised virtual microscopes. Quantitative analysis was performed using a theory test (which assessed preparation, theory knowledge and understanding) as well as a spotter test (which assessed identification skills, reasoning, and recall). Qualitative analysis was performed using a structured questionnaire and focus group discussions. Results: Modules using VM were better when compared with those using TM, showing statistically significant and better grades. Qualitative analysis performed, yielded important information as to how this technology can serve as a good adjunct to traditional histology classes in the competency-based curriculum by increasing student interest, enabling self-study, and reducing students dependence on the tutor. Conclusions: VM forms a good adjunct as well as a standalone modality of learning to TM, as it improves accessibility to slides and promotes self-learning.

Implementation of the Richmond Agitation-Sedation Scale (palliative version) on an inpatient palliative care unit.

BACKGROUND: The Richmond Agitation-Sedation Scale - Palliative version (RASS-PAL) tool is a brief observational tool to quantify a patient's level of agitation or sedation. The objective of this study was to implement the RASS-PAL tool on an inpatient palliative care unit and evaluate the implementation process. METHODS: Quality improvement implementation project using a short online RASS-PAL self-learning module and point-of-care tool. Participants were staff working on a 31-bed inpatient palliative care unit who completed the RASS-PAL self-learning module and online evaluation survey. RESULTS: The self-learning module was completed by 49/50 (98%) of regular palliative care unit staff (nurses, physicians, allied health, and other palliative care unit staff). The completion rate of the self-learning module by both regular and casual palliative care unit staff was 63/77 (82%). The follow-up online evaluation survey was completed by 23/50 (46%) of respondents who regularly worked on the palliative care unit. Respondents agreed (14/26; 54%) or strongly agreed (10/26; 38%) that the self-learning module was implemented successfully, with 100% agreement that it was effective for their educational needs. CONCLUSION: Using an online self-learning module is an effective method to engage and educate interprofessional staff on the RASS-PAL tool as part of an implementation strategy.

Short- and Long-Term Retentivity of Knowledge by Various Teaching Methods in Medical Education and Perception of Students Towards Them: A Comparative Study in a Medical University Hospital of Northern India.

BACKGROUND: To develop doctors with appropriate knowledge of health and diseases, reasonable medical abilities, and a positive attitude toward patients and their families, it is important to reexamine the methods used to educate and train medical school students. To establish which is best for both medical students and professors, the various teaching and learning methodologies must be compared and analyzed. This study attempts to determine the preferred medical education techniques among medical students as well as the caliber of the classes they attend. METHODS: This is a before-and-after study conducted among 480 first- (240) and second-year (240) undergraduate students. Students were divided into three groups. Each group was assigned a teacher who was responsible for teaching four short topics according to the common understanding and knowledge level of both year students in four different ways: traditional blackboard method, offline PowerPoint presentation, online PowerPoint presentation, and online annotative. Application-based learning and self-learning were the other two teaching methods conducted in a monitored environment. An MCQ-based pre- and post-test were taken to assess the improvement, and a feedback form was filled out by each student to assess their perception. To assess long-term retention, a surprise follow-up test was conducted after 15 days. RESULTS: For all the teaching methods except for traditional blackboard and online presentation, there was a significant improvement in the post-test scores as compared to the pre-test scores (p<0.05). Retentivity was more remarkable in online application-based and self-learning methods. 77.2% of the study participants preferred offline presentation as the mode of teaching. CONCLUSION: Retention was found to be highest in self-directed and application-based learning. So, students should be encouraged and motivated for self-study after every lecture, whatever the teaching method used by teachers.

Critical Reflexivity and Positionality on the Scholar-Practitioner Continuum: Researching Women's Embodied Subjectivities in Sport.

The sports world has many prejudices that have been converted into common sense. Some relate to the idea of athletes being strong or pretty but endowed with little intelligence. There is another view, perhaps a little more accurate, around the reification of consciousness in the name of the automation and maximum outcome of the body. Both views are informed by Cartesian thinking, perpetuating the mind-body dichotomy. Such a dichotomy is spread in several other areas in our society, expressed as binaries. We meet a binary when conducting research as well, disembodying the researcher as someone who is neutral, objective, and highly rational, and someone who, in synthesis, performs good mental work, but who must not let feelings intrude. On the contrary, we argue that we are embodied beings who are often not able to (and maybe should not) become detached from previous experiences and knowledge when conducting research. Even though this can present itself as a challenge, we consider that a fluid non-binary positioning encompasses actions holistically and leads to tasks being performed on a continuum. The purpose of this paper is to explore the reflexive process embedded in carrying out a PhD project committed to studying the production of the embodied subjectivities of a group of women high-level athletes in karate. The researcher inserted in the researched environment was not a high-level athlete; however, she had several experiences competing at the amateur level in different countries and faced experiences that were, to some extent, similar to those of the elite athletes. She used her previous experiences as a karateka, researcher, and woman to inform her research-doing since the intersectional social issues faced by her and lived queer feelings motivated her research questions. She plunged into a process of self-reflection and counted on the guidance of the other authors to organise her learning in order to use it in her scholarship. That was, primarily, an experience of "practice" of subjectivity through examining others' production of subjectivity, besides strengthening a positionality that lacked self-confidence. Thus, we explore issues around the researcher-practitioner theoretical-practical continuum of research-doing, presenting a journey that became empowering.

Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter.

To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation.

Assessing the utility of pre-course assessment and voluntary pre-work in two required integrated pharmacotherapy courses.

BACKGROUND AND PURPOSE: Self-assessment and self-learning are essential skills for student pharmacists. Data demonstrating the association between these skills in pharmacy courses are limited. The aim of this study was to evaluate the impact of providing pre-course review and administering a pre-course assessment on performance in two required integrated pharmacotherapy (IP) courses - IP: Pulmonology and IP: Cardiology. EDUCATIONAL ACTIVITY AND SETTING: This study included second-year student pharmacists enrolled in fall semester IP: Pulmonology and IP: Cardiology from 2019 to 2021. Voluntary pre-course review materials and pre-course assessments were added in fall 2021. Overall course grades and examination scores between each year were analyzed. Student perceptions of the pre-course assessment were also captured. FINDINGS: Of the 454 students analyzed, there was no difference in median overall IP: Pulmonology grades (85.93%, 86.67%, 86.29%; P = .63) or IP: Cardiology grades (80.25%, 78.3%, 79.96%; P = .41) for 2019, 2020, and 2021, respectively. IP: Pulmonology Exam 1 scores were statistically higher in 2021. For IP: Cardiology, Exam 1 and Final Exam scores were statistically higher in 2020 compared to 2019 and Exam 3 scores were significantly higher in 2021 than 2019. Pre-course assessment scores had a statistically significant, positive association with overall course grade. Half of the students surveyed agreed that completing the course prep work was an effective approach to learning. SUMMARY: Although overall course grades did not differ between years, pre-course assessment scores correlated with overall course grade. Thus, voluntary pre-course assessments could provide early identification of poor performance.

Análisis de las horas de trabajo autónomo al implementarse el sistema de créditos en odontología / Analysis of self-employed hours when implementing the credit system in dentistry

Introducción: El Sistema de Créditos Transferibles fue desarrollado para traducir la carga de trabajo de los estudiantes en créditos académicos que son reconocidos en todos los países, apuntando al logro del aprendizaje. El aprendizaje autónomo es un objetivo que la mayoría de los programas educativos promueven como una opción estratégica para conectar la profesión, el entorno de estudio y las expectativas profesionales. Objetivo: Analizar las horas de trabajo autónomo utilizadas por los estudiantes para lograr los resultados de aprendizaje determinados en los programas de asignaturas, su efectividad en cuanto al rendimiento académico y su correspondencia con lo establecido en el plan de estudio, ajustado al Sistema de Créditos Transferibles. Métodos: Se realizó un análisis descriptivo transversal retrospectivo a partir de datos de un registro manual de estudiantes (n = 54) y docentes (n = 6) respecto a seis asignaturas de primer nivel de la Carrera de Odontología de la Universidad Autónoma de Chile, sede Temuco. Resultados: Los análisis revelan una incoherencia entre las horas de trabajo autónomo utilizadas por los estudiantes con respecto a lo establecido en el plan de estudio y las horas de trabajo extra-aula estimadas por los profesores. Conclusión: Se concluye que la implementación del Sistema de Créditos Transferibles por sí sola no asegura una mejora en el desempeño de los estudiantes, requiriendo revisar el procedimiento institucional para definirlas; por parte de los docentes una mayor apropiación de los resultados de aprendizajes y la didáctica necesaria para orientar a los estudiantes a obtener un mayor rendimiento del trabajo autónomo, por otro lado, los estudiantes deben ser responsables del uso consciente de dichas horas(AU)

Introduction: The Transferable Credit System was developed to translate student workload into academic credits that are recognized in all countries, aiming at learning achievement. Autonomous learning is an objective that most educational programs promote as a strategic option to connect career, study environment and professional expectations. Objective: Analyzing the hours of autonomous work used by students to achieve the learning outcomes determined in the subject programs, their effectiveness in terms of academic performance and their correspondence with what is established in the study plan, adjusted to the Transferable Credit System. Methods: A retrospective cross-sectional descriptive analysis was carried out using data from a manual record of students (n= 54) and teachers (n= 6) regarding six first level subjects of the Dentistry course of the Universidad Autónoma de Chile, Temuco campus. Results: The analysis revealed an incoherence between the hours of autonomous work used by the students with respect to what is established in the study plan and the hours of extra-classroom work estimated by the professors. Conclusion: It is concluded that the implementation of the Transferable Credit System alone does not ensure an improvement in student performance, requiring a review of the institutional procedure to define them; on the part of teachers a greater appropriation of the learning outcomes and the didactics necessary to guide students to obtain a higher yield of autonomous work, on the other hand, students must be responsible for the conscious use of these hours(AU)

Introduction and roll-out of self-learning App for midwifery during the COVID-19 pandemic and its sustainability in Cambodia.

Similar to other countries, coronavirus disease (COVID-19) pandemic significantly impacted not only the ability of midwives to deliver high quality maternal care, but also their ability to access professional development opportunities, including in-service training in Cambodia. In response, we developed a Cambodian version of Safe Delivery App (SDA), aligned to Cambodia's clinical guidelines. The SDA is a free digital job aid and learning platform for skilled birth attendants developed by Maternity Foundation that works offline and is used in more than 40 countries after adapting to the country context. In the year and a half since its launch in June 2021, SDA has become established in Cambodia, with more than 3,000 people, accounting for nearly half the number of midwives in Cambodia, downloading and using it on their devices, and 285 people having completed its self-learning modules. The review of the introduction process revealed that publicity on the professional association's social networking sites, in-person in-depth hands-on training, and troubleshooting in a managed social networking group were useful in promoting the use of the application, and that the Continuing Professional Development Program accreditation has been a strong motivator for completing the self-study program. On the other hand, the COVID-19 pandemic has led to increased use of digital tools, but it is important to prevent the expansion of the digital divide when implementing new digital tools, including SDA.

'Internet+' comprehensive nursing training course in the post-epidemic era-an exploration of the mixed teaching mode: a randomized trial.

Objective: To explore the effect of the application of the 'Internet+' nursing teaching mode on the comprehensive teaching 'Fundamentals of Nursing'. Trial design: Parallel design and convenient sampling were used to select vocational nursing students from the Nursing College of Capital Medical University. Methods: Selected students were randomly divided into two groups. The control group consisted of 30 students in Grade 2020 higher vocational nursing education (traditional teaching mode). The observation group consisted of 30 students in Grade 2021 higher vocational nursing education (Internet+ mixed teaching mode). Training assessment results, automatic learning ability, professional identity, and satisfaction were compared between the two groups. Results: Compared with the control group, the students in the observation group scored higher in the following operation practices: venous blood sampling, intradermal injection, cardiopulmonary resuscitation (CPR), sputum aspiration, and putting on and taking off robes (84.01 ± 0.87 vs. 92.14 ± 1.23; 91.41 ± 0.82 vs. 96.86 ± 0.27; 87.56 ± 0.31 vs. 93.91 ± 2.79; 88.11 ± 0.51 vs. 93.75 ± 0.29; and 82.29 ± 0.29 vs. 90.96 ± 0.34, respectively, with p < 0.05 for all scores). The total scores for autonomous learning ability and subjective satisfaction were also higher in the observation group compared with the control group (82.98 ± 4.72 vs. 93.17 ± 5.01 and 96.67% vs. 90.00%, respectively, with p < 0.05 for all scores). Conclusion: In the post-epidemic era, the 'Internet+ hybrid teaching mode' was applied to comprehensive nursing teaching. This changed the traditional education mode, which focuses only on professional knowledge. The 'Internet+' teaching mode results showed that the professional, ideological, and political courses exhibited the same value guidance, which improved students' independent learning ability, practical operation ability, professional identity, and satisfaction.

Metodología 'SLIDE-4-U' para implicar al alumnado en el desarrollo de la clase presencial / How to involve students in the development of the face-to-face class: the 'SLIDE-4-U' methodology

Introducción: Desde hace unos años, tanto en grupos grandes como pequeños, y principalmente en clases en línea, se hapuesto en práctica la metodología ‘SLIDE-4-U’ o ‘una diapositiva para ti’ (2020PID-UB/023), con el objetivo de implicar alestudiante en su propio proceso de aprendizaje y en el de sus compañeros. Se consiguió mediante la participación delalumnado en la explicación en clase de diapositivas específicamente diseñadas para este fin. Métodos: La experiencia se llevó a cabo en el primer semestre del curso 2021-22 en la asignatura Nutrición Molecular delgrado de Nutrición Humana y Dietética (Universitat de Barcelona). Se preparó una sesión de seminario presencial centrada en inmunonutrición. El profesor dirigió la sesión seleccionando de forma aleatoria al estudiante, que debía explicar ladiapositiva sin preparación previa. Las explicaciones del alumnado fueron complementadas o corregidas por el profesordurante el desarrollo de la actividad. Al final del seminario se realizó una encuesta de opinión en la que se constató labuena aceptación de esta iniciativa (puntuaciones medias superiores a 4,2 sobre 5). Resultados: El alumnado consideró que era un reto explicar una diapositiva sin prepararla previamente y que este hecho,asociado a no saber quién haría la explicación, había provocado un cierto clima de nerviosismo. Ahora bien, la mayoríaestaba de acuerdo en que los esquemas/imágenes aportados fueron suficientes para poder desarrollar la actividad y quelas explicaciones hechas por los compañeros eran suficientemente correctas. Asimismo, también valoraban positivamente la participación del profesor a la hora de completar las explicaciones de sus compañeros. En general, la metodologíautilizada hizo que el alumnado fuera más consciente de que las diapositivas tienen una estructura y un objetivo, y de ladificultad de comunicar correctamente...(AU)

Introduction: Lately, both in large and small groups and mainly in online classes, the 'SLIDE-4-U' or 'one slide for you' methodology (2020PID-UB/023) has been put into practice, with the aim of involving the student in their own learning process and that of their classmates. It is achieved through the participation of the students in the explanation of slides in class, specially designed for this purpose. Methods: The experience was carried out in the first semester of the 2021-22 academic year in the subject Molecular Nutrition of the Human Nutrition and Dietetics degree (Universitat de Barcelona). A face-to-face seminar session focused on immunonutrition was prepared with this type of material. The teacher led the session by randomly selecting the student, who had to explain the slide without prior preparation. The explanations of the students were complemented and/or corrected by the teacher, during the development of the activity. At the end of the seminar, an opinion survey was carried out in which the good acceptance of this initiative was verified (average scores higher than 4.2 out of 5). Results: The students considered that it was a challenge to explain a slide without previously preparing it, and that this fact, associated with not knowing who would do the explanation, had caused a certain climate of nervousness. However, the majority agreed that the diagrams/images provided were sufficient to be able to carry out the activity and that the explanations made by the classmates were correct enough. Likewise, they also positively valued the teacher's participation when completing the explanations of their classmates. In general, the methodology used made the students more aware that the slides have a structure and an objective, and of the difficulty of communicating correctly...(AU)

Percepción de satisfacción de los estudiantes frente al uso del Team-Based Learning en línea y su relación con el rendimiento académico / Perception of student satisfaction with the use of Team-Based Learning online and its relationship with academic performance

Introducción: Es necesario adaptar técnicas didácticas que favorezcan el logro de competencias de modo virtual, sincrónico o asincrónico, en previsión de situaciones contingentes socialmente complejas que dificulten la presencialidad, como ha sido la actual pandemia de COVID-19. Objetivo: Identificar la percepción de satisfacción de los estudiantes sobre el uso del Team-Based Learning (TBL) como una metodología activa y participativa en la enseñanza remota de emergencia y su relación con el rendimiento académico en estudiantes de la carrera de Tecnología Médica de la Universidad Católica de la Santísima Concepción (UCSC). Sujetos y métodos: Se realizó una intervención pedagógica en el curso de Fisiología Humana de la Facultad de Medicina de la UCSC, en la cual se empleó el TBL como método de apoyo al aprendizaje de contenidos entregados en clases sincrónicas en línea en dos unidades teóricas: renal y digestivo. La recolección de datos sobre percepción de satisfacción se realizó a través de una encuesta de elaboración propia. Las calificaciones se obtuvieron del registro académico del curso. Se realizó un análisis estadístico descriptivo-correlacional. Resultados: Se encontró aceptable consistencia interna de los diversos ítems de la escala de percepción de satisfacción. Se describió una mejor percepción para los ítems de metodología y autorregulación del aprendizaje, con una mayor correlación positiva para los estudiantes con mejor rendimiento académico. Conclusiones: El TBL, como técnica didáctica para favorecer la interacción y el aprendizaje en contextos tradicionales, parece susceptible de mantener esta característica favorecedora de interacción y aprendizaje cuando es adaptada a condiciones complejas socialmente, como ha sido el contexto de pandemia de COVID-19.(AU)

Introduction: It is necessary to adapt didactic techniques that favor the achievement of skills in a virtual, synchronous or asynchronous way, in anticipation of socially complex contingent situations that make it difficult to attend, as has been the current COVID-19 pandemic. Objective: To identify the perception of student satisfaction regarding the use of Team-Based Learning (TBL) as an active and participatory methodology in emergency remote teaching and its relationship with academic performance in students of the Universidad Católica de la Santísima Concepción (UCSC) Medical Technology major. Subjects and methods: A pedagogical intervention was carried out in the Human Physiology course of the UCSC School of Medicine, in which the TBL was used as a method of supporting the learning of contents delivered in synchronous online classes in two theoretical units: renal and digestive. The collection of data on the perception of satisfaction was carried out through a self-developed survey. The grades were obtained from the academic record of the course. A descriptive-correlational statistical analysis was performed. Results: Acceptable internal consistency of the various items of the perception of satisfaction scale was found. Better perception was reported for the items of methodology and self-regulation of learning, with a greater positive correlation for those students with better academic performance. Conclusions: The TBL as a didactic technique to favor interaction and learning in traditional contexts, seems likely to maintain this characteristic that favors interaction and learning, when it is adapted to socially complex conditions such as the context of the COVID-19 pandemic.(AU)