Evaluation Of Training Research Articles

Program Evaluation of Student Leadership Basic Training: Analysis of Effectiveness, Cost, and Benefits

Program Evaluation of Student Leadership Basic Training: Analysis of Effectiveness, Cost, and Benefits

Translation and Validation of the Dutch Version of the Spiritual Care Competence Questionnaire (SCCQ-NL)

The importance of spirituality in the treatment of mental illness is increasingly acknowledged, but mental healthcare professionals often feel they lack specific competence. An instrument is missing to quantify the spiritual care competence of mental healthcare professionals in the Netherlands. The aim of this study was thus to translate the Spiritual Care Competence Questionnaire (SCCQ) into Dutch and validate it for use in mental healthcare. After translation, the SCCQ-NL was distributed in a cross-sectional design among 3497 healthcare professionals in two mental healthcare institutions (MHIs) in the Netherlands. In the sample of 730 completed questionnaires, exploratory factor analysis revealed seven factors: perception of spiritual needs competencies, team spirit, spiritual self-awareness, documentation competencies, empowerment and proactive opening competencies, knowledge about other religions, and conversation competencies. One item was deleted during the process. Internal consistency for the 25-item SCCQ-NL subscales is sufficient with Cronbach’s alpha ranging from 0.64 to 0.81. Conversation competencies and perception of spiritual needs scored highest in the sample, next to knowledge about other religions and empowerment competencies, while spiritual self-awareness, team spirit and documentation competencies scored the lowest. Small but significant differences in several subscale scores were found for profession, identifying oneself as a believer, practicing prayer and/or meditation, age and working years. The SCCQ-NL can be used for the assessment of spiritual care competencies and for the planning and evaluation of training and improvement strategies.

Putting conceptions and considerations of culture into practice: A qualitative evaluation of a cultural competence training.

Putting conceptions and considerations of culture into practice: A qualitative evaluation of a cultural competence training.

Cognitive and physiological evaluation of virtual reality training in nursing

Virtual reality (VR) has increasingly been used in several disciplines, including nursing, a profession in high demand that is now facing a shortage. This study investigated the effectiveness and efficacy of VR hybrid training over traditional training (TR) methods. Sixteen college students were recruited and randomly assigned to get 100% TR or 50% TR and 50% VR (VR50). Participants attended a three-day program with a registered nurse, consisting of lectures and practical lab sessions. Participants’ performance, training time, cognitive development, physical development, mental workload, user experience, Students’ Satisfaction and Self-Confidence, and team learning were evaluated. The results showed that the VR50 performed as well as, and sometimes even better than the TR group (p-value = 0.043). VR50 group significantly had higher cognitive development and found VR easy to use and attractive (p-value < 0.05). VR-integrated training makes nurses’ training more affordable and accessible while providing instant and relevant feedback. Practitioner Summary: This study assessed employment-integrated virtual training in nursing, particularly peri-care, by comparing the performance, cognitive, physical, and mental workload of traditional and integrated VR training groups. The findings of this study provide significant support for incorporating VR training into educational settings.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Critical Evaluation of Training and Development of Employees: A Study with Reference to BPO Companies in Chennai City

Training and Development is a mix of training as well as development. It focuses more on overall development of employees in the business concern. Training and Development is an effective tool through which performance of employees is improved besides improving group performance. Training has significant role in teaching employees of sharpening skills, concepts, changing of attitude and gaining more knowledge to enhance performance of the employees. Training should be efficient enough so that it would help the employees of their development subsequently a company can also improve in the long run.

Evaluating the effectiveness of training of managerial and non-managerial bank employees using Kirkpatrick’s model for evaluation of training

This research employs Kirkpatrick’s model, to assess the efficacy of training programmes for managerial and non-managerial employees in the banking sector using all the four components i.e., Reaction, Learning, Behavior and Results. Data were collected from 402 respondents from public, private and foreign sector banks. SEM-PLS was used to determine the relationship between the levels of the Kirkpatrick’s model for the evaluation of the training programs of the baking sector. The results suggest that all the four levels are interlinked and Kirkpatrick model was effective to evaluate the impact of training programs on employee motivation and bank performance. Reactions of employees (stage 1) have enhanced knowledge, and skills and has a positive and significant influence on learning (stage 2) and Behavior through job performance (stage 3) has a positive impact on results (stage 4). The results reflected that the adjusted R2 = 0.732 of the managerial level is more than that of the non-managerial level (R2 = 0.571). This indicates that the training is more effective on the Managerial level than the Non-Managerial level and that the managerial employees are more skilled and experienced in their jobs. The study is novel and one of the initial contributions to apply Kirkpatrick’s model to banking sector in India.

Prediksi Kelulusan Tepat Waktu Siswa SMK Teknik Komputer Menggunakan Algoritma Random Forest

School performance can be measured through student completion rates, which is a key indicator. Low graduation rates indicate problems in the education and learning system, which require timely intervention to prevent students from not completing their education. Therefore, predicting graduation rates is crucial for schools in order to determine students who are likely to not complete their education, so as to provide early assistance to improve their academic performance. This research is urgent because by understanding and predicting student completion, schools can allocate resources more effectively to support at-risk students, with the ultimate goal of improving graduation rates and overall school performance. This research uses random forest algorithm with graduation dataset. The distribution of training and test data selection uses stratified random sampling method to ensure a balanced representation of each class generated. The Random Forest model was successfully obtained through training and model evaluation using test data, showing an accuracy of 1.0 or equivalent to 100%. The use of the random forest algorithm on student graduation datasets can be an effective approach in supporting timely graduation prediction due to its high accuracy and the model's ability to recognize both graduating and non-passing students.

Peningkatan Keterampilan Bisnis Pelaku UMKM Handycraft melalui Pelatihan Digital Marketing dan Keuangan

This Community Service activity targets productive community groups, namely handicraft MSMEs, Kapas District, Bojonegoro Regency. Based on the results of problem identification, it is known that the priority problems are 1) integrated digital marketing and 2) financial literacy. The training implementation method uses tutorial and lecture techniques. The target to be achieved from the implementation of this service is to gain knowledge and understanding for partners regarding digital marketing and financial literacy for handicraft MSMEs, Kapas District, Bojonegoro Regency. The results of the training evaluation in Klampok Village, Kapas Bojonegoro District for UKM Handycraft partners show that partners need support to improve their marketing through the use and management of digital marketing as well as changing the mindset that partners need to carry out good business financial management and family financial management so that family prosperity can be achieved.

IJCM_293A: A Reflective study about knowledge assessment and feedback of medical officers involved in the training programme for management of Animal bite as per NRCP guidelines &amp; Snakebite in Tertiary HealthCare Centre in Central India

Background: A training program for Management of Animal-bite as per NRCP (National Rabies Control Program) guidelines &amp; Snakebite of Medical Officer was conducted in the Tertiary Healthcare Centre. Evaluation of such training has been rarely reported. Objective: The objective of this study was to reflect on the quality of this training program regarding knowledge assessment and feedback. Methodology: A pre-test and post-test cross-sectional assessment of knowledge of medical officers working at health facilities of Bhopal district of Madhya Pradesh government, who attended one-day training program at AIIMS Bhopal in 2023, were conducted through the same questionnaire having 18 single-answer multiple choice questions. Anonymous feedback was also taken through a semi-structured questionnaire. Data were analyzed using R statistical software. The Chi-square test and Wilcoxon signed-rank test was used to compare the pre-and post-test proportions of correct responses and scores, respectively. Results: Out of a total of 54 participants, 64.8% were males. The median (IQR) age was 41 (31,52) years. The majority of the participants (68.5%) had the qualification of MBBS, 14.8% had PG degree, and the rest, 16.6%, had BAMS degree. Median (IQR) pre and post-test scores were9.0 (9.0, 12.0) and 15 (12, 16.0) respectively. All participants agreed that the training was very useful for the patient care and gave positive feedback related to the management of training. Conclusion: This training was very useful in terms of improvement of knowledge is concerned. More Tertiary Healthcare institutions should take the initiative to conduct such training programs for capacity building of Medical Officers working at community level.

Comparing Graduating Plastic Surgery Residents' Case Logs With Accreditation Council for Graduate Medical Education Requirements, Content at National Meetings, and In-Service Examination Test Items.

The importance of adaptable and up-to-date plastic surgery graduate medical education (GME) has taken on new meaning amidst accelerating surgical innovation and increasing calls for competency-based training standards. We aimed to examine the extent to which the procedures plastic surgery residents perform, as represented in case log data, align with 2 core standardized components of plastic surgery GME: ACGME (Accreditation Council for Graduate Medical Education) minimum procedure count requirements and the PSITE (Plastic Surgery In-Service Training Examination). We also examined their alignment with procedural representation at 2 major plastic surgery meetings. Nine categories of reconstructive and aesthetic procedures were identified. Three-year averages for the number of procedures completed in each category by residents graduating in 2019-2021 were calculated from ACGME national case log data reports. The ACGME procedure count minimum requirements were also ascertained. The titles and durations of medical programming sessions scheduled for Plastic Surgery The Meeting (PSTM) 2022 and the Plastic Surgery Research Council (PSRC) Annual Meeting 2022 were retrieved from online data. Finally, test items from the 2020 to 2022 administrations of the PSITE were retrieved. Conference sessions and test items were assigned to a single procedure category when possible. Percent differences were calculated for comparison. The distribution of procedures on plastic surgery resident case logs differs from those of the major mechanisms of standardization in plastic surgery GME, in-service examination content more so than ACGME requirements. Meeting content at PSTM and PSRC had the largest percent differences with case log data, with PSTM being skewed toward aesthetics and PSRC toward reconstructive head and neck surgery. The criteria and standards by which plastic surgery residents are evaluated and content at national meetings differ from the procedures they actually complete during their training. Although largely reflecting heterogeneity of the specialty, following these comparisons will likely prove useful in the continual evaluation of plastic surgery residency training, especially in the preparation of residents for the variety of training and practice settings they pursue.

Research on the Restrictive Factors and Strategies for Improving the Teaching Ability of University Teachers under the Background of Education Digitization

Education digitization deeply integrates modern information technology with education, innovates the traditional teaching mode of universities, and makes higher education glow with new vitality. At present, the digital transformation of higher education has become a research focus of higher education reform. Therefore, the digital teaching ability of university teachers has become an indispensable force. It is not only conducive to the construction of college teachers but also conducive to the improvement of university teaching quality, which is a key factor related to the quality of higher talent training in the new era. Therefore, whether university teachers can adapt to the development of digitalization and how to improve their digitalized teaching ability are worthy of attention. Based on this research background, this paper tries to summarize the new requirements of digital transformation of education for teachers' teaching ability by reading relevant literature of the Ministry of Education and taking TPACK theory as guidance. Then, combined with the existing investigation and research results, using case analysis and comparative research, this paper summarizes and analyzes the constraints that affect the development of teachers' abilities from both internal and external aspects of teachers. Finally, based on the analysis of the constraints, suggestions are put forward from the perspectives of teachers' teaching cognition, teaching behavior, school training organization, evaluation system, and digital construction.

429 Sub-internship Academy: Implementation and Evaluation of Single-Day Resident-Driven Focused Sub-internship Preparation Course

INTRODUCTION: Strong performance in neurosurgical sub-internships requires familiarity with clinical and technical skills which may not otherwise be covered in standard medical school curricula. Thus, a need exists for immersive and comprehensive sub-internship preparation programs, which should be optimized based on student feedback. METHODS: Academy curriculum involved hands-on and discussion-based elements split into three stations: (1) suturing and general information, including a suturing and knot tying workshop and discussion of expectations of sub-interns; (2) introduction to the operating room and drilling, covering patient positioning, surgical technology, OR etiquette, and craniotomy basics; and (3) cases and clinical skills, reviewing clinical imaging, focused neurological exams, and medical note writing. An anonymous online post-survey was administered to collect data on participants’ perceptions of the utility of the academy. RESULTS: Twelve medical students participated in the inaugural neurosurgery sub-internship academy. Nine responded to a post-survey, which revealed following ratings: the overall program was rated as having maximal impact on sub-internship readiness and enthusiasm for neurosurgery by eight (88.9%) and seven (77.8%) respondents, respectively, and trends were comparable for individual curriculum components. A largely positive impact on mentorship access was also observed. CONCLUSIONS: Student perceptions of the relevance and utility of the sub-internship academy were positive, and the program fostered enthusiasm for neurosurgery and opportunities for mentorship. Evaluation of the program’s actual impact on preparedness and sub-internship performance as well as student retention in the field of neurosurgery will be performed in the future, in accordance with the Kirkpatrick model of training evaluation.

의사결정나무분석을 활용한 중장년 근로자의 일자리 특성 분석

The study aimed to examine job characteristics for middle-aged workers using decision tree analysis. To do this, data from the 25th wave of the Korean Labor Panel (year 2022) was utilized, with a study sample 4,570. First, the factor with the most significant impact on employment according to gender was identified as 'work hours format.' When working full-time, the percentage decreased by 36.5% for women and increased by 63.5% for men. For part-time work, the percentage increased significantly by 85.5% for women and decreased by 14.5% for men. Second, the factor with the most significant impact on employment according to age range was identified as 'highest education level.' For those with a high school education or below, the percentage decreased by 38.2% for ages 40-49 and increased by 61.8% for ages 50-59. For those with a college degree or higher, the percentage increased by 66.0% for ages 40-49 and 70.0% for those with an associate degree. Third, summarizing the study results, the proportion of men is higher for individuals with a college degree or higher, working full-time, and having high job satisfaction. On the other hand, the proportion of women is higher for those working part-time and having a high level of welfare benefits. Additionally, for individuals with an associate degree, stable employment, and job satisfaction, the proportion of those in their 40s is higher. For those with a high school education or below, working in non-office jobs, and having unstable employment, the proportion of those in their 50s is higher. Given that middle-aged workers are more likely to withdraw from the labor market as they age, continuous evaluation of educational training and policy support for job sustainability is necessary. There is a need for support to maintain quality employment even after reemployment.

Comparative study on the performance of ConvLSTM and ConvGRU in classification problems—taking early warning of short-duration heavy rainfall as an example

Convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) are two widely adopted deep learning models that combine recurrent mechanisms with convolutional operations for spatiotemporal sequences forecasting. To clarify the convergence speed and classification ability of the above two models, using the same model architecture to predict the same classification problem is necessary. This research treats the district-level warning of short-duration heavy rainfall in Beijing as a binary classification problem in deep learning, and composite radar reflectivity data of the Beijing–Tianjin–Hebei radar network and rainfall data from automatic weather stations in Beijing are used for training and performance evaluation. The results show that the convergence speed of ConvGRU is approximately 25% faster than that of ConvLSTM. The early-warning performances of ConvLSTM and ConvGRU have similar trends with region, time, and rain intensity, but most of the scores of ConvLSTM are higher, and in a few cases, ConvGRU has higher scores.摘要卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元, 通过将循环机制与卷积运算相结合, 常常用于时空序列的预测. 为了明确上述两种模型的收敛速度和分类能力, 需要使用相同的模型架构对相同的分类问题进行预测. 本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题, 使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估. 结果表明, ConvGRU的收敛速度比 ConvLSTM快约25%. ConvLSTM和ConvGRU的预警性能随地区, 时间, 降雨强度的变化趋势相似, 但大部分ConvLSTM的得分较高, 少数情况下ConvGRU的得分较高.

Evaluation of occupational health and safety training for nursing students

Introduction. Nurses, who constitute the largest part of the healthcare team, start to encounter many occupational exposures from the beginning of their education and this exposure continues throughout their professional lives.&#x0D; Aim. The aim of the study is to evaluate the effect of occupational health and safety education on the knowledge levels of second year nursing students.&#x0D; Materials and Methods. The sample of the quasi-experimental research with a single-group pre-test post-test design consisted of 261 second-year nursing students. The data of the research were collected with the "Descriptive Characteristics Questionnaire" and the "Occupational Health and Safety Training Questionnaire" between March-June 2018.&#x0D; Results. The average age of the students participating in the research is 20.87±1.31. When the pre-test and post-test occupational health and safety knowledge levels of nursing students were compared, it was found that the rates of knowing the occupational health and safety law correctly, knowing the responsibilities of the employees, knowing the occupational hazards for the health workers, and knowing what to do to prevent the sharps injuries were statistically significant. level was found to increase (p &lt;0.05).&#x0D; Conclusion. In the study, in which the effect of occupational health and safety education given to second-year nursing students on occupational health and safety knowledge levels was evaluated, it was determined that the education provided an increase in the students' knowledge of occupational health and safety law, occupational hazards and protection from occupational hazards for healthcare workers, notification of occupational accidents and waste management.

Gender Classification through Fusion of Holistic and Region-based Facial Patterns

In this study, a robust gender prediction system is proposed to fuse global and regional facial representations through score and feature level fusion. In order to extract facial features for gender classification, Binarized Statistical Image Features (BSIF) approach is applied on holistic and regional features of face images. The extracted features are then concatenated to combine the region-based information at feature level fusion. Then the optimized sub-set of features is selected using Particle Swarm Optimization (PSO) method. Finally, the holistic and regional features are combined at score level fusion to produce the final set of scores for gender classification. This study applies Weighted Sum (WS) rule strategy for score level fusion. The experimental results are performed on Multiple Biometric Grand Challenge (MBGC) and CASIA-Iris-Distance databases with consideration of subject-disjoint training and testing evaluation to testify the validity of the proposed gender classification system. The experimental results of the study demonstrate the success of the proposed scheme for gender prediction.

Construction of a Sustainable Training System for Engineering and Technological Innovation Talents Based on CIPP Model in the Digital Era

With the advent of the digital era, the demand for engineering and technological talents in the market is increasing. It is crucial to design a sustainable training and evaluation system suitable for engineering and technological innovation talents. However, traditional data evaluation models have problems with unsuitable evaluation indicators and low model accuracy. Therefore, the paper selects the decision-making-oriented evaluation model to build the basic talent training evaluation indicators, and design expert questionnaires based on the Delphi method to screen indicators. Moreover, the paper also combines the adaptive genetic algorithm optimised backpropagation neural network to establish a talent cultivation evaluation model, and conducts simulation experiments using MATLAB to verify its feasibility. The results showed that the evaluation accuracy [Formula: see text] value of the research model was 0.99635, and the fitness value was 1.34, which was 0.5 higher than the unmodified model and achieved good evaluation results. At the same time, by comparing with the traditional genetic algorithm optimised model and the unimproved backpropagation model, the average evaluation accuracy of the research model increased by 66.44% and 13.59%, and the recall rate increased by 10.79% and 23.96%. The research model has improved the accuracy of evaluation, and its adaptability has also been enhanced, achieving superior evaluation results, which has important value in the cultivation of engineering and technological innovation talents.

Supplemental Material for Putting Conceptions and Considerations of Culture Into Practice: A Qualitative Evaluation of a Cultural Competence Training

Supplemental Material for Putting Conceptions and Considerations of Culture Into Practice: A Qualitative Evaluation of a Cultural Competence Training

Assessment of Power System Stability During Transients Using Deep Residual Shrinkage Network and CBAM Integration

Aiming at the noise that may exist in the data collection and transmission of power system synchronous phasor measurement unit (PMU), as well as the imbalance between stable and unstable samples, making the model likely to tilt to most kinds of samples, thus affecting the evaluation results of power system transient stability model, a transient stability assessment method integrating Convolutional Block Attention Module (CBAM) and deep residual shrinkage network is proposed. First, the mapping relationship between the input values and the labels of the stability results is established, and the electrical quantities are transformed into the form of convolutional feature maps to be input into the model. Second, channel attention module and spatial attention module are introduced, and soft threshold function is used to automatically learn the noise threshold and reduce the interference of the noise. As the model tends to favor the majority of the samples during training, gradient harmonized mechanism (GHM) loss function is introduced to improve the attention of the minority of samples as well as the training effect and evaluation performance of the model. Finally, the model is tested by simulation on the IEEE39 node system.