Training Methodology Research Articles

Effects of high-intensity interval training on selected indicators of physical fitness among male team-sport athletes: A systematic review and meta-analysis.

Superior physical fitness and performance are essential in male team sports. Among a myriad of training methodologies, high-intensity interval training (HIIT) has gained popularity owing to its unparalleled efficiency and effectiveness. Previous studies have established that HIIT is a proven and effective approach for enhancing various physiological performance outcomes, particularly oxygen consumption capacity, in individual sports. Despite potential differences in training practices between male and female athletes, HIIT is recognized as an anaerobic training approach for team-sport athletes. This systematic review aimed to comprehensively and innovatively analyze the existing literature to examine the effectiveness of HIIT on oxygen consumption performance among male team-sport athletes. A comprehensive literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across the PubMed, SCOPUS, Web of Science, and SPORTDiscus databases until December 31, 2023. The inclusion criteria for this review encompassed research articles published in peer-reviewed journals that specifically focused on the impact of HIIT on the oxygen consumption performance of male players engaged in team sports. The study population exclusively consisted of male participants. The collected data included study characteristics, participant demographics, intervention details, and outcomes. Methodological quality assessment was performed using standardized criteria. The effect sizes (ESs) were calculated, and a meta-analysis was conducted using a random-effects model. The literature search yielded 13 eligible studies encompassing 286 athletes aged 14-26 years. The meta-analysis showed statistically significant enhancements in maximal oxygen uptake (VO2max) in six studies (ES, 0.19-0.74; p < 0.005), Yo-Yo Intermittent Recovery Test (YYIRT) performance in six studies (ES, 0.20-2.07; p = 0.009), repeated-sprint ability total time (RSAtotal) in five studies (ES, 0.18-1.33; p < 0.001), and the best and average times for repeated-sprint ability (RSAbest and RSAmean, respectively) in four studies (ES, 0.47-1.50; p < 0.001). However, two studies did not report any significant differences in the outcomes of the Velocity in 30-15 Intermittent Fitness Test (VIFT) between the experimental and control groups (ES, -0.08 and -0.27; p = 0.87 and 0.443, respectively). Moreover, one study did not report any significant differences in the maximal aerobic speed (MAS) (ES, 0.41, p = 0.403). HIIT significantly improved VO2max, YYIRT, and RSA; however, it did not appear to enhance VIFT and MAS performance, irrespective of age or competition level. These findings indicate that HIIT could serve as a valuable method for improving oxygen consumption performance (VO2max, YYIRT, and RSA) in male team-sport athletes, offering a time-efficient alternative to the traditional training methods. Further research is warranted to investigate its impact on performance outcomes in competitive settings and identify optimal HIIT protocols tailored to specific team sports.

Exploring the Efficacy of the Production-Oriented Approach in Improving English Interpreting Skills—A Case Study

The demand for efficient English interpreters have been on a constant rise in the rapid evaluation of globalization—the skilled interpreters are a prerequisite for international interactions across diplomatic, business, and cultural domains. The traditional models in practice to train the interpreters are based on theoretical knowledge with less emphasis on practical skills required for effective real-world communication. In contrast, the Production-Oriented Approach (POA) focuses more on Practical Language Application (PLA), considered a better alternative to conventional training methodologies. This study challenges to analyze the effectiveness of the POA in improving English Interpreting Skills (EIS) by creating a case study at a top university in China. The work employed a quantitative research design on a study involving 47 students in pre-test/post-test experiments to measure the boosts achieved in EIS direct features to POA implementation. Results indicated significant improvements in the experimental groups, with undergraduates and graduates showing mean score increases of 13 and 12 points, respectively, after the implementation of POA.

Public Hospital Workers' Views on the Process of Switching from the Hospital Information Management System to the e-Health Information System in a Province

The purpose of this study is to investigate the views, reasons for satisfaction, and difficulties encountered by medical staff members regarding the Hospital Information Management System (HIMS) in state hospitals located in Amasya and its surrounding areas. It also assesses the knowledge and experiences of healthcare professionals with e-Health apps in public hospitals, emphasizing their opinions and recommendations for assistance, training, adaptation, and involvement throughout the integration of these applications into healthcare services. The goal of the study is to better understand how healthcare professionals feel about digital technologies and to provide suggestions for raising the caliber of healthcare services. The Amasya Provincial Health Directorate oversees four facilities where the research was carried out. Following permission by the ethics committee, data gathering was underway on April 22, 2024, with a pilot research carried out in April of the same year. There are 913 healthcare workers in the population, and a minimum sample size of 405 at a 95% confidence interval was found to be appropriate. Using the'stratified, simple random' approach, the sample was selected and split into three strata based on occupational groupings. With a median age of 37, 38.8% of the 405 participants were female. 34.2% of physicians, 33.7% of midwives, nurses, and health officers, and 40.4% of medical secretaries and data entry staff expressed satisfaction when HIMS satisfaction was analyzed. The system's capacity to decrease errors and enhance workflow was one of the primary factors contributing to user satisfaction. Furthermore, 42.7% of individuals worked nights. Compared to other professional groups, physicians utilized e-health applications like e-prescription and e-report more frequently. Physicians were more likely to employ telemedicine than other healthcare professionals, who were less knowledgeable in this field. The study recommends developing training methodologies specific to various professional groups and expanding the use of e-Health applications. The study highlights the need for regular in-service training, a more robust technical infrastructure, and user-friendly interfaces to boost healthcare personnel' satisfaction with e-Health. In summary, this study offers important information for strengthening e-Health and HIMS applications and presents important suggestions for raising the caliber of healthcare services through digital transformation.

Muscular Endurance Training: Bridging Gym Practices with Science Recommendations

This study investigated the effectiveness of training methodologies for improving muscular endurance in young adult males and whether these practices align with scientific recommendations. A cross-sectional descriptive comparative study was conducted with 115 male participants aged 18 to 26. Data on various training parameters were collected using a self-administered questionnaire. The results indicated that participants exhibited discrepancies in their training practices compared to established scientific recommendations. An important percentage of study participants did not exercise according to the recommended guidelines regarding exercise sequencing, intensity, repetitions, rest periods, and training supervision. Participants who followed the recommendations regarding intensity, sets, repetitions, weekly training frequency, and supervision achieved higher objective accomplishment scores. Thus, there are notable discrepancies between everyday gym practices and scientific recommendations for improving muscular endurance in young adult men. Adherence to current recommendations and professional supervision are essential for maximizing training effectiveness, preventing injuries, and facilitating future adaptations.

Stress tests and biomarkers of resilience: Proceedings of the second state of resilience science conference.

The "Stress Tests and Biomarkers of Resilience" conference, hosted by the American Geriatrics Society and the National Institute on Aging, marks the second in a series aimed at advancing the field of resilience science. Held on March 4-5, 2024, in Bethesda, Maryland, this conference built upon the foundational work from the first conference, which focused on defining resilience across various domains-physical, cognitive, and psychosocial. This year's gathering centered around three factors: the biology that underlies resilient outcomes; the social, environmental, genetic, and psychosocial factors that impact that resilience biology; and the biomarker testing and imaging that predicts resilient outcomes for older adults. The presentations and discussions around these topics were underscored by considerations around the many impacts of social determinants of health on resiliency interventions, and by advances in the modern training and research methodologies that influence data collection and experiment design.

Deep learning-based Covid-19 diagnosis: a thorough assessment with a focus on generalization capabilities

The Covid-19 pandemic has significantly spurred the development of deep learning (DL) models for the pathology automatic diagnosis based on CT scan images. However, the assumption about the generalization of the proposed models remains to be assessed and shown for concrete clinical use. In this work, we have investigated the real value of widely used public datasets for the elaboration of DL models that are dedicated to automatic diagnosis of Covid-19 using CT scans. We have collected various international public datasets from 13 countries. Different Convolutional Neural Networks (CNNs) have been trained and their performances carefully assessed. Two evaluations have been conducted: (1) an internal evaluation following a cross-validation procedure, and (2) an external evaluation on real patients coming from new and different sources. The objective is to assess the generalization capabilities considering real-world conditions: different acquisition conditions, devices and configurations. Three families from the most effective CNN models have been selected (ResNet, DenseNet and EfficientNet). These have been fine-tuned, evaluated and used within a training methodology based on transfer learning. The most effective models have been further customized in order to create new models that are dedicated to the task at hand. These models have significantly improved the diagnosis performance.

Neural Machine Translation (NMT): Deep learning approaches through Neural Network Models

Abstract. This paper explores the significant advancements in Neural Machine Translation (NMT) models, focusing on the impact of different architectures, training methodologies, and optimization techniques on translation quality. The study contrasts the performance of Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and the Transformer model, highlighting the superior capabilities of the Transformer in handling long-range dependencies and providing contextually accurate translations. Key optimization techniques, such as learning rate scheduling, dropout regularization, and gradient clipping, are discussed in detail, emphasizing their roles in enhancing model performance and training efficiency. Furthermore, the paper presents a comparative analysis of NMT and traditional Statistical Machine Translation (SMT) systems, showcasing NMT's superior BLEU scores and fluency. The application of model distillation is also examined, demonstrating how smaller models can achieve high performance with reduced computational resources. These findings underscore the transformative potential of NMT in achieving state-of-the-art translation quality and efficiency.

Influence of Mental Model-derived Themes on the Quality of Radiological Emergency Response by HAZMAT Technicians: Insights from a Case Study.

This paper investigates the link between gaps in emergency responders' notions of mental model regarding radiation and risk and their effectiveness in responding to radiological incidents. Particularly, this work focused on exploring themes that emerged in prior work related to improper understanding and application of knowledge concepts related to radiation risks and Radiological Dispersal Device (RDD) scenarios (Leek et al., 2024b). The research uses a quantitative approach to correlate various thematic elements, such as responders' confidence levels, comprehension, and application of radiation risk principles, with the quality of the emergency response score gained through a virtual reality simulation. The results underscore a strong effect of responders' confidence level on response quality scores. Additionally, the study identifies that improper understanding of knowledge concepts and incorrect application of radiation risk and RDD concepts are factors that detract from the quality of response, especially the tendency to overestimate health risks associated with a 25-rem (0,25 Sv) dose and to misapply principles of radiation risk. The implications of this research are significant for the development and refinement of training programs for hazardous materials (HAZMAT) technicians and other emergency responders. The findings suggest the need for a comprehensive review of current training methodologies to address the identified deficiencies that had impacts on the quality of response. The findings provide a foundation for reshaping training priorities and operational readiness, driving the development of training that is both grounded in empirical evidence and that directly addresses the knowledge gaps influencing response quality. The methodological framework developed and employed, including the quality scoring system and the Expected Mental Model State (EMMS) Diagnostic Matrix, also hold potential for broader application in future investigations, extending to diverse types of responders and emergency scenarios.

SIGN-LANGUAGE TRANSLATOR USING MACHINE LEARNING

This research introduces a cutting-edge system designed to facilitate communication between individuals who are deaf and those who are hearing. By employing sophisticated machine learning algorithms and specific convolutional neural networks (CNNs), the proposed system can effectively sign language gestures into text or speech. A meticulously curated collection of sign language gestures serves as the foundation for training the model, ensuring its proficiency in accurately classifying several hand shapes and positions. To optimize classification performance, the system incorporates data preprocessing techniques that highlight the most distinctive features of the hands, thereby streamlining the computational process. This paper provides a comprehensive overview of the system's architecture, training methodology, and evaluation results, emphasizing the critical role of machine learning in developing inclusive communication tools that empower the deaf community. Future research initiatives will focus's on expanding the gesture dataset and refining real-time processing capabilities to further enhance the system's effectiveness and accessibility. Key Words: CNN, TensorFlow, Keras, NLTK

Detection, classification, and characterization of proximal humerus fractures on plain radiographs.

The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%). For detection and classification, the algorithm was trained on 1,709 radiographs (n = 803), tested on 567 radiographs (n = 244), and subsequently externally validated on 535 radiographs (n = 227). For characterization, healthy shoulders and glenohumeral dislocation were excluded. The overall accuracy for fracture detection was 94% (area under the receiver operating characteristic curve (AUC) = 0.98) and for classification 78% (AUC 0.68 to 0.93). Accuracy to detect greater tuberosity fracture displacement ≥ 1 cm was 35.0% (AUC 0.57). The CNN did not recognize NSAs ≤ 100° (AUC 0.42), nor fractures with ≥ 75% shaft translation (AUC 0.51 to 0.53), or with ≥ 15% articular involvement (AUC 0.48 to 0.49). For all objectives, the model's performance on the external dataset showed similar accuracy levels. CNNs proficiently rule out proximal humerus fractures on plain radiographs. Despite rigorous training methodology based on CT imaging with multi-rater consensus to serve as the reference standard, artificial intelligence-driven classification is insufficient for clinical implementation. The CNN exhibited poor diagnostic ability to detect greater tuberosity displacement ≥ 1 cm and failed to identify NSAs ≤ 100°, shaft translations, or articular fractures.

Strategies for constructing harmonious colour combinations by art students with autism spectrum disorders

Introduction. The survey discussed in the article represents the first experience of an empirical study of the peculiarities characterising the ways of construction and perception of colour harmony by students with autism spectrum disorders – professional art education learners. The relevance of the study is conditioned by new trends in education related to the need to create an inclusive and favourable environment for people with autism. This environment is necessary for their successful integration and professional training in art specialities that are considered a promising area for professional realisation of people with autism. Methods. The survey was carried out using the experimental method involving 21 participants with autism spectrum disorders (mean age = 20.4 years; SD = 2.4; 9 males, 12 females). The participants’ answers in the experimental group were compared with those of an age-, gender-, and education-matched control group (N=21). The participants’ task was to match two given shades with a third one in the palette, for the colour combination to be as harmonious as possible. The experimental palette allowed to change the hue, lightness and chroma of shades. Results. The participants with autism spectrum disorders were twice as likely to choose yellow (23.8% compared to 12.4% in the control group) and purple shades (16.2% compared to 9.5%); hardly used orange (1.9%) and blue (4.8%) and preferred lighter shades (the mean L value was 67.8 compared to the control group’s 63.0). The chroma of the selected shades correlated with their hue and lightness. The stimulus selection strategy depended on the distance (∆E*00) between the given (upper and lower) shades in the CIELAB colour space and was based on a specific “geometry” (a particular ratio of values characterising the angles and side lengths formed by the given and selected shades, as well as a preference for a particular triangle area and its type). Discussion. The experiment showed that the students with autism spectrum disorders differed markedly from neurotypical students in a number of indicators when constructing a colour harmony. The experimental group participants’ responses showed a “preference for sameness” characteristic of autism. The significant inter-group differences were applied more to the lightness and chroma of the chosen shades than to their hue. The shades preferred by the participants with autism spectrum disorders better matched the conditions of the habitual “chromatic diet” and the colour’s statistics in nature. The observed specificity may also reflect the reduced ability to generalise and average out colours in terms of holistic perception, specific to autism. Conclusion. The survey results make an important contribution to the improvement of the professional training methodology for art students, and for the development of scientifically based educational approaches to teaching people with autism spectrum disorders, primarily in the context of the formation of colour-related competencies; in addition, they may provide new impulses for investigating the fundamental mechanisms of cognitive processing of colour-related information.

A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role.

The Effect of Virtual Reality Technology in Table Tennis Teaching: A Multi-Center Controlled Study.

This study investigated the effectiveness of virtual reality (VR) technology in table tennis education compared to traditional training methods. A 12-week randomized controlled trial was conducted with 120 participants divided equally between VR and traditional training groups. Performance metrics, learning motivation, and satisfaction were assessed at regular intervals. Results demonstrated significant advantages of VR training, with the VR group showing superior improvements in serve accuracy (23.5% vs. 15.8%, p < 0.001), rally endurance (an increase of 8.2 vs. 5.7 shots, p < 0.01), and overall skill scores (18.7 vs. 13.2 points improvement, p < 0.001). The VR group also exhibited higher increases in learning motivation (23.5% vs. 12.8%, p < 0.001) and satisfaction (31.5% vs. 18.7%, p < 0.001). Subgroup analysis revealed particular benefits for novice players and younger participants. These findings suggest that VR technology offers a promising approach to enhance table tennis education, potentially revolutionizing sports training methodologies. Future research should focus on long-term skill retention and the optimization of VR training protocols.

Developing Generalizable Scoring Functions for Molecular Docking: Challenges and Perspectives.

Structure-based drug discovery methods, such as molecular docking and virtual screening, have become invaluable tools in developing novel drugs. At the core of these methods are Scoring Functions (SFs), which predict the binding affinity between ligands and protein targets. This study aims to review and contextualize the challenges and best practices in training novel scoring functions to improve their accuracy and generalizability in predicting protein-ligand binding affinities. Effective training of scoring functions requires careful attention to the quality of training data and methodologies. We emphasize the need for robust training strategies to produce consistent and generalizable SFs. Key considerations include addressing hidden biases and overfitting in machine-learning models, as well as ensuring the use of high-quality, unbiased datasets for both training and evaluation of SFs. Innovative hybrid methods, combining the advantages of empirical and machine-learning approaches, hold promise for outperforming current scoring functions while displaying greater generalizability and versatility.

AI and VR Integration in E-Learning: Designing meaningful learning environments

The main idea of the educational process based on artificial intelligence and virtual reality forms a new, effective methodology of training. This article examines how AI facilitates VR environments and enriches them with immediate feedback, learner-generated learning paths and big data, resulting in personalizing the education requirements for a spectrum of learners. The literature review addresses the subject through different areas where AI and VR have been applied in a timely manner to learning and comprehension, as well in fields such as engineering, healthcare, and humanities. Also, the advantages of this integration, including enhanced activity, access to the material, and potential to get practical training, are outlined. But, it is imperative to also consider the implementation costs, technology enforcement issues, as well as the probable data privacy problems. Direction and opportunity are explored, along with such areas as further development of adaptive learning environments, more realistic VR settings, and challenges of data utilization in learning. Finally, this article underlines the valuable possibility of the AI and VR applying in the educational field and the necessity of overcoming the barriers to the educational standards.

Integrated Care in Parkinson’s disease: e-training for multidisciplinary team members

Abstract Background Parkinson’s is a high prevalence disease worldwide and training in Integrated Care (IC) for professionals in this field is an obvious need. The SUPER training course is focused on integrated care applied to Parkinson’s disease (PD) and aimed at social and health professionals. Both the content and the training methodology have been co-designed with people living with PD, relatives/caregivers and professionals from Spain and Italy. The course comprises3 training modules (10 units), alongside self-assessment questions, as well as case studies. Accessible open access via an e-learning platform. It is available in English, Spanish, Italian and French enhancing its applicability and transferability. Objectives The SUPER e-training course aims to equip members of multidisciplinary teams with knowledge, skills and competencies in IC applied to PD. Results The course was validated with 89 socio-health professionals in Spain and Italy (M age = 37.28; 75.28% women), who completed a questionnaire assessing the usefulness and quality of the training modules and the platform, as well as an overall evaluation of the training in terms of quality and impact. According to the them: the training could improve the quality of care offered to people living with PD; it is expected to heighten interest in IC, while also introducing novel and invaluable elements. Conclusions This course is useful to train professionals of different profiles (and European contexts) on IC in PC, increasing their knowledge and skills and improving the provision of care to people living with PD. The SUPER multilingual course is an innovative tool because it has been designed from a bottom-up approach and makes available to any professional a free training that can be taken online at any time. Key messages • SUPER training course equips professionals from a holistic, multidisciplinary and person-centred approach to PD. • This multilingual tool aids diverse professionals in social &amp; health care.

Problems of formation and development of digital literacy of university students in non-core training directions

Introduction. The article substantiates the relevance and feasibility of studying the problems of formation and development of digital literacy of university students in non-core areas of training. The need to find ways to effectively and efficiently manage the formation and development of digital competencies of future specialists is emphasized.Purpose setting. The purpose of the study is to analyze the problems of formation and development of digital literacy of university students in non-core areas of professional training.Methodology and methods of the study. The methodological basis was an integrative approach to understanding the processes and results of the formation and development of digital literacy of university students in non-core areas of professional training, conceptual provisions and principles of competency- based and personal-activity approaches. The leading research methods are theoretical analysis and synthesis of research results on the problems of formation and development of digital literacy of university students in non-core areas of training, of conditions and measures to intensify and optimize the processes and results of professional education in the context of the formation and development of digital literacy of students, to highlight the conditions and measures to support the development of digital literacy of students of non-core specialties.Results. The author summarizes the results of research into the conditions and factors for the formation and development of digital literacy among university students in non-core areas of training. The importance and promise of systematic, holistic work in the context of the formation and development of digital literacy of university students in non-core areas of training is noted: creating conditions and taking a system of steps to ensure the achievement of digital literacy of future specialists.Conclusion. The results of the scientific research carried out and the presented conclusions expand pedagogical knowledge about the theory and methodology of vocational training in the specific conditions of the modern educational environment: its intensive and consistent digitalization, stratification and individualization. Based on the conducted research, we can conclude that it is necessary to emphasize students’ understanding that digital competencies can help him solve and prevent a number of important problems in his life, starting with problems of professional demand and competence and ending with problems of general self-realization and self-actualization. The prospects of the study are related to the need to further search for effective and efficient models for the formation and development of digital literacy of future specialists.

Advancing Organizational Effectiveness Through Strategic Workforce Planning and Technology Integration

Abstract: This comprehensive examination delves into the pivotal roles of strategic workforce planning and technological advancements in bolstering organizational effectiveness. Spanning various critical dimensions such as human resource demand forecasting, strategic recruitment and selection, employee training and development, diversity and inclusivity, and the integration of technology in HR practices, the paper articulates a multi-faceted approach to optimizing workforce capabilities. By leveraging predictive analytics, organizations can forecast future employment needs, while AI-enhanced recruitment processes streamline candidate selection, ensuring alignment with organizational goals. The deployment of advanced training methodologies, coupled with a focus on creating a diverse and inclusive work environment, not only enriches organizational culture but also drives innovation and improves overall performance. The paper further explores the strategic use of HR analytics, AI in recruitment, and digital platforms in employee development and engagement, highlighting their significance in adapting to the evolving work landscape. Through an in-depth analysis, it becomes evident that the integration of strategic HR planning and cutting-edge technologies is not merely an option but a necessity for organizations aiming to thrive in the competitive global market.

Challenges library and information science (LIS) postgraduate students encounter in research topic selection

This study examines research topic selection among LIS postgraduate students in Tanzania. A quantitative approach was used to collect the data, and purposive sampling was used to recruit a total of 105 respondents. Key findings showed that research topic selection is influenced by how extensively the topic has been researched, postgraduates’ personal interests and whether the topic has been previously researched by a student. Insufficient research knowledge, little global LIS research exposure and insufficient supervisory leading roles affect research topic selection. Findings showed male and doctorate postgraduate students face more challenges in LIS research topic selection. The study concludes that LIS postgraduates’ research topic selection is challenging due to insufficient research methodology training and foundations. The study suggests research methodology training for supervisors and students, revising LIS curricula and appropriate research guidance to LIS postgraduates would be important in addressing the challenges found. The study also recommends the establishment of an LIS national repository centre with research content accessible to all students.

Quantification of training in educational methodology among teachers on the degree course in medicine: a pilot study

BackgroundMedical education has undergone significant changes over the last decades. Scientific and technological progress alongside contemporary society’s changing requirements have driven demand for highly trained, competent doctors. In response to this need, university faculties of medicine have sought innovative forms of teaching and evaluating the students on their degree courses. The aim of this study was to quantify the characteristics and extent of academic training in teaching methods, of participation in innovative teaching initiatives, and training in simulation and debriefing among the teaching personnel on the degree course in medicine at the University of Granada (Spain).MethodsThis transversal descriptive study was conducted among a population of 121 educators teaching on the medical degree course at the University of Granada, Spain. All responded to a specially designed CoRe-Content Representation questionnaire. This consisted of various parts: (a) demographic data; (b) teaching experience and qualifications; (c) specific information about training in teaching skills received. The Fisher test was applied whenever the dependent variable had two values (dichotomous) and the Chi-square test when it had more than two values (polytomic). Statistical significance was established with an alpha error of 5%.ResultsThe results showed that 87.60% of the educators had received no training in debriefing. There was a notable gender gap, whereby women held fewer management posts, fewer were engaged in clinical activity, and fewer had undergone training in clinical simulation. Teachers with degrees in medicine had undergone less regulated training than educators with other degree qualifications.ConclusionThe main areas of medical training that require improvement (and so present challenges to be met in the years to come) are as follows: a definitive solution to the existing gender gap, general implementation of new educational models and methods (especially learning based on clinical problem-solving and simulation), closing generation gaps, and improved training processes for educators with clinical attachment.