Rigorous Training Research Articles

Using Existing Resources to Create a Successful End-of-Life Doula Program

The end-of-life doula role has emerged as an integral part of the interdisciplinary team caring for patients. The doulas provide an extra layer of support from a non-clinical lens to prevent crises, follow-up with families, and guide them through their journey. This manuscript describes how a hospice agency developed a performance improvement project to train volunteers to become end-of-life doulas through a rigorous training program, based on the Doula Model of Care. The implementation of the program was evaluated based on caregiver and staff satisfaction, and volunteer knowledge competency. The results from the pilot program showed mixed outcomes for patient and staff satisfaction, but created a rigorous training program for hospice volunteers. The COVID-19 pandemic disrupted data gathering and implementation of the program, so outcomes were varied. However, the program remains successful with ongoing training of the end-of-life doulas and an increase in new volunteers to support the program.

BP neural network multi-module green roof thermal performance prediction model optimized based on sparrow search algorithm

Green roofs, as a passive building energy-saving technology, offer a range of benefits including reduced energy consumption and mitigation of the urban heat island effect. To analyze the thermal performance of green roofs more accurately and efficiently, this study introduces a novel predictive model that utilizes a Sparrow Search Algorithm (SSA) optimized Backpropagation Neural Network (BPNN) for forecasting the thermal performance of green roofs in subtropical regions. Compared to traditional BPNN, the SSA-BPNN model enhances the network's convergence and generalization capabilities by optimizing initial weights and thresholds. The model, established using meteorological data from Guangzhou, China, and simulated green roof temperature data, underwent rigorous training and validation. The results indicate that predictive accuracy has significantly improved across all seasons, with substantial reductions in all 12 evaluation metrics, and the model GR3 (SSA-BP) emerging as the optimal model. Particularly in summer, the SSA-BPNN model exhibits the highest accuracy, underscoring its effectiveness for subtropical climates. The findings suggest that the SSA-BPNN model is a powerful tool for sustainable urban planning and green roof design in subtropical regions, marking a significant advancement in the integration of artificial intelligence in environmental engineering.

Deformation mechanism-assisted deep learning architecture for predicting step-like displacement of reservoir landslide

Reservoir landslides in the Three Gorges Reservoir, China, exhibit prolonged slow motion and the potential for catastrophic events due to fluctuations in reservoir levels and intense rainfall episodes. Their distinct step-like deformation characteristics, involving rapid transformation processes of different states, pose challenges for accurate early warning and prediction. Previous forecasting models have often struggled with limited accuracy. This study introduces a mechanism-assisted deep learning model, leveraging the Informer architecture, to predict prolonged step-like reservoir landslide displacement. Utilizing a 15-year continuous monitoring dataset of the Baishuihe landslide, this model investigates the landslide mechanism, identifies influencing conditions underlying the step-wise behavior, and customizes input features for the prediction model by integrating optimized variational mode decomposition and wavelet analysis. Additionally, the dynamic correlation and hysteresis analysis between triggering factors and displacement offer valuable physical insights into the model and enhance the interpretability of the model. The model is further tailored to accommodate features of the monitoring dataset associated with landslide evolution by integrating a global multi-head attention mechanism and pooling layers, enabling the capture of both globe dependencies and local critical features of the model inputs. Through rigorous model training, performance evaluation, and tuning, the proposed model efficiently predicts step-wise landslide displacement, particularly during short-term rapid transitions between creep-mutation states.

Analysis and Prediction of Bird Migration Using RFA and GBR

The project "Analysis and Prediction of Bird Migration Using RFA and GBR" utilizes an extensive eBird dataset with approximately 100,000 observations to analyze and forecast bird migration patterns. The primary goals involved data collection, preprocessing, and feature extraction, followed by the progression of predictive models Gradient Boosting in use and Random Forest algorithms. Data preprocessing encompassed cleaning, normalization, and encoding to ensure the dataset’s quality and relevance. Through rigorous training and validation, our models demonstrated high accuracy and precision. The resulting analysis uncovered notable migration patterns and seasonal trends that correspond to known bird behaviors. These findings highlight the capabilities of artificial intelligence in deepening our knowledge of bird migration and contributing to effective conservation efforts. It also highlights the value of incorporating additional environmental data to further refine predictive models, offering a robust framework for analyzing and predicting migration patterns and other ecological phenomena. Furthermore, the project demonstrated the critical role of machine learning in environmental science, demonstrating how sophisticated algorithms can uncover complex ecological connections previously challenging to identify. The findings from This project could aid policymakers and conservationists in developing targeted conservation efforts, ensuring better protection of migratory bird species amidst changing climatic conditions. Future work could expand on this foundation by integrating real-time data streams and leveraging more sophisticated ensemble methods to enhance the migration forecasts.

Semi-supervised lung adenocarcinoma histopathology image classification based on multi-teacher knowledge distillation.

In this study, we propose a semi-supervised learning scheme using a patch-based deep learning framework to tackle the challenge of high-precision classification of seven lung tumor growth patterns, despite having a small amount of labeled data in whole slide images (WSIs). This scheme aims to enhance generalization ability with limited data and reduce dependence on large amounts of labeled data. It effectively addresses the common challenge of high demand for labeled data in medical image analysis.

Approach. To address these challenges, the study employs a semi-supervised learning approach enhanced by a dynamic confidence threshold mechanism. This mechanism adjusts based on the quantity and quality of pseudo labels generated. This dynamic thresholding mechanism helps avoid the imbalance of pseudo-label categories and the low number of pseudo-labels that may result from a higher fixed threshold. Furthermore, the research introduces a multi-teacher knowledge distillation technique. This technique adaptively weights predictions from multiple teacher models to transfer reliable knowledge and safeguard student models from low-quality teacher predictions.

Main results. The framework underwent rigorous training and evaluation using a dataset of 150 WSIs, each representing one of the seven growth patterns. The experimental results demonstrate that the framework is highly accurate in classifying lung tumor growth patterns in histopathology images. Notably, the performance of the framework is comparable to that of fully supervised models and human pathologists. In addition, the framework's evaluation metrics on a publicly available dataset are higher than those of previous studies, indicating good generalizability.

Significance. This research demonstrates that a semi-supervised learning approach can achieve results comparable to fully supervised models and expert pathologists, thus opening new possibilities for efficient and cost-effective medical images analysis. The implementation of dynamic confidence thresholding and multi-teacher knowledge distillation techniques represents a significant advancement in applying deep learning to complex medical image analysis tasks. This advancement could lead to faster and more accurate diagnoses, ultimately improving patient outcomes and fostering the overall progress of healthcare technology.&#xD.

A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology

Among a variety of virtual metrology models, dynamic generative latent variable models (DGLVMs) have proven to be an effective tool, owing to outstanding advantages in dealing with complicated data characteristics including temporal correlations, variable colinearities, high dimensionality, and process and measurement uncertainties. Presently, the vast majority of DGLVMs are developed upon the assumption that data are Gaussian-distributed, which is however, non-robust against outlying data and leads the DGLVMs to compromised performance, as the Gaussian distribution is susceptible to outliers that would skew the estimated parameters of DGLVMs. In view of this, this paper proposes a semi-supervised robust learning (SsRL) framework for the DGLVMs by designing an ad-hoc fully robust model structure that handles the outlying data by means of the heavy-tail properties of Student’s t distribution. In addition, the SsRL-DGLVM framework is initiated as a concrete model using probabilistic slow feature analysis (denoted RoSsPSFA) for which a rigorous training algorithm without resorting to any approximations is developed. The performance of the RoSsPSFA is thoroughly evaluated by both numerical and real-world industrial cases, showing the RoSsPSFA’s better immunity against outlying data and higher generalization accuracy.

Tomographic examination of the head model through image reconstruction from measurement data

This study aims to integrate ultrasound tomography with numerical algorithms to significantly enhance brain sensing capabilities for diagnosing critical brain abnormalities. Advanced ultrasound tomography, employing a high-frequency transducer array, captures intricate brain structures. The echoes processed by multi-channel receivers allow for three-dimensional imaging. Deep learning models, particularly convolutional neural networks, undergo rigorous training on extensive datasets. Hyperparameter tuning and regularization are key to model optimization. Algorithms handle large datasets, detecting subtle pathological changes in ultrasound images. The system demonstrates proficient image reconstruction and analysis. Implementing deep learning algorithms rectifies operator-dependent inconsistencies and imaging artifacts. The analysis shows significant improvements in diagnostic accuracy and processing time. The convergence of ultrasound tomography and deep learning faces challenges such as image quality variation, computational demands, and clinical integration. Despite these, the enhanced image clarity and the ability to conduct real-time analytics are promising. The study sets a new standard in neurological diagnostics, indicating the potential for sophisticated diagnostic tools to become accessible in diverse healthcare settings.

Multi-class segmentation of temporomandibular joint using ensemble deep learning

Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the temporomandibular joint, coupled with the variability in magnetic resonance image quality, often hinders an accurate diagnosis. To surmount this challenge, we developed deep learning models tailored to the automatic segmentation of temporomandibular joint components, including the temporal bone, disc, and condyle. These models underwent rigorous training and validation utilizing a dataset of 3693 magnetic resonance images from 542 patients. Upon evaluation, our ensemble model, which combines five individual models, yielded average Dice similarity coefficients of 0.867, 0.733, 0.904, and 0.952 for the temporal bone, disc, condyle, and background class during internal testing. In the external validation, the average Dice similarity coefficients values for the temporal bone, disc, condyle, and background were 0.720, 0.604, 0.800, and 0.869, respectively. When applied in a clinical setting, these artificial intelligence-augmented tools enhanced the diagnostic accuracy of physicians, especially when discerning between temporomandibular joint anterior disc displacement and osteoarthritis. In essence, automated temporomandibular joint segmentation by our deep learning approach, stands as a promising aid in refining temporomandibular joint disorders diagnosis and treatment strategies.

SOME CONSIDERATIONS REGARDING THE ORGANIZATION ND THE ACTIVITY OF THE PUBLIC NOTARIES IN ROMANIA

Public notaries in Romania play an essential role in the legal and economic system of the country. They are responsible for the authentication of the legal documents, ensuring that comply with the law and that the rights and obligations of the parties involved are clearly defined and protected. Their activity is regulated by Law no. 36/1995, the law of public notaries and notarial activity.In the current economic and social context, public notaries contribute to the safety and stability of civil and commercial transactions, thus facilitating confidence in the market. They ensure the transparency and fairness of these transactions, which is crucial for the efficient functioning of the economy.The professionalism and impartiality of public notaries are guaranteed by rigorous training and strict compliance with deontological and legislative norms. Thus, public notaries in Romania are fundamental for ensuring legal certainty and for protecting the rights of citizens and legal entities.

Advancing breast ultrasound diagnostics through hybrid deep learning models

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the “EfficientKNN” model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous cross-validation.Our model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms—SGD, Adam, and RMSprop—with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent overfitting.The results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's scalability, efficient use of cloud storage, and real-time prediction capabilities, all while minimizing computational demands.By integrating the strengths of EfficientNetB3's deep learning architecture with the interpretability of k-NN, EfficientKNN presents a significant advancement in medical image classification, promising improved diagnostic accuracy and clinical applicability.

Implementing a proposed framework for enhancing critical thinking skills in synthesizing AI-generated texts

Since the development of open and low-cost generative artificial intelligence (GenAI), the higher education community has witnessed high use of AI-generated texts in scholarly research and academic assignments, attracting ongoing debate about whether such practices constitute cheating. While scholars argue that integrating GenAI tools can enhance productivity, critics raise concerns about the negative effect of such integration on critical thinking (CrT). This study therefore proposed a framework for enhancing students’ CrT skills in synthesizing AI-generated information. The proposed framework is underpinned by various theoretical foundations, encompassing five interconnected step-wise phases (familiarizing, conceptualizing, inquiring, evaluating, and synthesizing). The study was conducted under two separate experiments. The first experiment (Study 1) validated the effectiveness of the proposed framework, providing CrT training to 179 postgraduate students. In the second study (n = 125), additional experiments were undertaken to confirm the effectiveness of the framework in different contexts. An experimental procedure involving pretest and posttest design was implemented wherein participants were randomly allocated to one of three groups: experimental group 1 (exposed to our framework), experimental group 2 (exposed to an alternative self-regulated learning framework), and a control group (exposed to a non-structured framework). Results from Study 1 revealed that the framework enhances students’ CrT skills to synthesize AI-generated texts. However, these CrT skills manifested through various rigorous training aimed at reinforcing learning. While the proposed framework holds considerable value in cultivating CrT skills, significant differences arise across various personality traits. In Study 2, the framework proved to be effective in different contexts. However, it did not make a difference, particularly in its capacity to enhance students’ self-regulated learning compared to other frameworks. We discussed the implications of the findings and recommended it to educators seeking to prepare students for the challenges of the AI-driven knowledge economy.

The impact of a curriculum-based research training program on medical students’ research productivity and future research interests: a longitudinal study

BackgroundIncorporating scientific research into undergraduate medical education is necessary for the quality of future health care. However, providing rigorous research training to a large number of medical students at one institution remains one of the major challenges. The authors studied the impact of a curriculum-based Research Training Program (RTP) for all undergraduate students at Zhejiang University School of Medicine (ZUSM) on research productivity and future research interests.MethodsMedical students (n = 2,213) from ZUSM who completed the course of RTP between 2013 and 2020 were studied. The authors measured the academic performance, research publications, and research projects of students across years, and evaluated potential factors that contribute to student research productivity and increased interest in future research.ResultsAcross the years, there was an increase in the number of student publications, a greater proportion of students with publications, and a greater proportion of projects involving three or more students (P < .01 for all). The academic performance of the course was associated with increased publications (P = .014), whereas overall satisfaction of the course (OR 2.07, 95% CI [1.39, 3.10], P < .001), Skill Composite Score (SCS) (OR 1.70, 95% CI [1.16, 2.50], P = .007), and male gender (OR 1.50, 95% CI [1.06, 2.12], P = .022) were associated with increased future research interests.ConclusionsThe findings suggest that the curriculum-based RTP improved students’ research productivity, and that overall program satisfaction and self-assessed performance were associated with increased students’ intent to participate in future research.

TSMD-Net: A two-stage mixed dehazing network with feature fusion and multi-window self attention on Jetson board

Single image dehazing is a pivotal endeavor focused on enhancing the quality of images by removing haze-induced noise. Achieving this goal requires a delicate equilibrium between retaining spatial intricacies and contextual understanding, particularly crucial in real-world scenarios where complexity is amplified. In response to this challenge, we present the Two-Stage Mixed Dehazing Network (TSMD-Net), designed to reconcile these conflicting objectives by leveraging the foundational U-Net architecture. Our approach entails a dual-phase strategy: initially assimilating contextual comprehension through encoder-decoder frameworks, followed by amalgamating it with high-resolution branches to uphold spatial integrity. To streamline the analysis and comparison of this architecture, we opt to substitute or eliminate nonlinear activation functions with multiplication operations, effectively mitigating system complexity. Furthermore, to foster seamless information exchange across hierarchical layers of the encoder-decoder at varying scales, we introduce the Feature Fusion (FF) mechanism, facilitating the integration of insights from higher layers progressively down to the base layer. This amalgamated data significantly enhances the fidelity of the original image transformation by harmonizing the inclusion of contextual feature maps with the preservation of spatial details, resulting in noise-free, high-quality images. Additionally, we propose the “Multi-Window Self Attention” (MWSA) mechanism, characterized by linear time complexity, as the central block of the encoder-decoder. This module mitigates the confined receptive fields inherent in convolutional neural networks (CNNs), enabling the aggregation of more comprehensive feature-map data. Subsequently, our TSMD-Net undergoes rigorous training and testing on both synthetic and real-world datasets, showcasing superior performance compared to current state-of-the-art (SOTA) networks while demanding fewer processing resources. To validate the robustness and practical utility of our methodology, we implement TSMD-Net on a low-end Jetson Nano board, confirming its effectiveness and efficiency in real-world settings.

Preparing Residents to Navigate Neurosurgical Careers in the 21st Century: Implementation of a Yearlong Enhanced Didactics Curriculum

Background and ObjectiveNeurosurgery residency, known for its rigorous training, must adapt to evolving healthcare demands. Formal education should now encompass areas like quality improvement and patient safety (QIPS), machine learning, career planning, research infrastructure, grant funding, and socioeconomics. We share our institution's experience with a yearlong enhanced didactics curriculum, complementing our traditional teaching. MethodsOur resident and faculty team evaluated essential skills for trainee success and leadership, identified knowledge gaps, and addressed them with 31 lectures. We conducted pre- and 6-month surveys using a Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree) to assess resident education. Survey results were analyzed using Student t-tests, with p < 0.05 indicating statistical significance. ResultsEleven out of twelve residents completed the pre- and 6-month surveys. The surveys revealed improved scores in areas such as research career preparation (3.0/5 to 4.33/5, p=0.002), building research skills (3.18/5 to 4.33/5, p=0.002), and comfort with quality and patient safety (4.09/5 to 4.75, p=0.04). Residents found the lectures highly effective in supplementing their residency training (4.58/5). Qualitative feedback from faculty was highly positive as well. ConclusionsOrganized neurosurgery excels in clinical and technical training for residents but lacks formalized training in crucial nonclinical areas, such as QIPS, machine learning/artificial intelligence, research infrastructure, and socioeconomics. Our formal curriculum focused on these topics, with positive resident engagement and feedback over the first six months. However, continuous longitudinal monitoring is needed to confirm the curriculum’s efficacy. This program may guide other neurosurgery departments in enhancing resident education in these areas.

Management of Exchange Rate Forecasting Through Vanilla Long Short-Term Memory Networks (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA)

Predicting foreign exchange rates presents a formidable challenge within financial forecasting, given its pivotal role in influencing a country's economic trajectory. To address this challenge, numerous forecasting models are employed with the aim of anticipating future exchange rate movements. This study aims to determine the efficacy of two prominent machine learning models, namely Vanilla Long Short-Term Memory Networks (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA), in forecasting the exchange rate between the Malaysian Ringgit (MYR) and the United States Dollar (USD). Employing Python's robust statistical packages for time series forecasting, both Vanilla LSTM and ARIMA models undergo rigorous training on the dataset. Leveraging Python's programming capabilities enables in-depth analysis, essential for model refinement and accuracy assessment. Upon comparing the error measures of both models, it becomes evident that the Vanilla LSTM model outperforms ARIMA, exhibiting lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. Specifically, the MSE and RMSE for Vanilla LSTM stand at 0.0102 and 0.1011, respectively, surpassing ARIMA's 0.0113 and 0.1062. Thus, affirming Vanilla LSTM's superiority in exchange rate forecasting. Consequently, the study concludes that Vanilla LSTM emerges as the most accurate model for exchange rate prediction, with a projected exchange rate of RM4.22 for July 2022.

SPIKES: Skills and Performance Inventory as Kinesiological Assessment for Empowerment of Sepaktakraw Athletes

This research presents a comprehensive assessment of Sepaktakraw athletes at Batangas State University (BatStateU) TNEU, aiming to enhance athlete development through evidence-based methodologies. Recognizing the global trend towards universities' pivotal role in athlete development, the study addresses the diverse landscape of sports programs, particularly focusing on Sepaktakraw in the Philippines. BatStateU's visionary 5-year development plan emphasizes creating champion athletes, aligning with global trends and emphasizing sports infrastructure and training programs. However, the study identifies an opportunity to enhance training programs by introducing the SPIKES program, incorporating kinesiological assessments for a more scientific and tailored approach. Using a descriptive survey methodology, the research integrates Physical Fitness and Skill-specific Performance Tests to evaluate Sepaktakraw athletes comprehensively. Anthropometric profiling revealed diverse body compositions, while physical fitness assessments highlighted variations in speed, agility, strength, and more. Skill-specific performance evaluations in serving, spiking, and game-specific skills provided insights into athletes' proficiency levels. Results categorized athletes into four kinesiological profiles (A, B, C, D), guiding the design of personalized training programs. The Skill-Specific Training Program, unfolding over 12 weeks, targets specific improvements in physical fitness and skills. This evidence-based approach offers a systematic framework for optimizing Sepaktakraw athletes' performance, providing valuable insights for coaches, trainers, and athletes alike. The study not only enhances BatStateU's athlete development initiatives but also serves as a model for personalized and scientifically rigorous training programs in Sepaktakraw and potentially other sports.

Policy Recommendations for Coordinated and Sustainable Growth of the Behavioral Health Workforce.

Policy Points Demand for behavioral health services outpaces the capacity of the existing workforce, and the unmet need for behavioral health services is expected to grow. This paper summarizes research and policy evidence demonstrating that the long-standing challenges that impede behavioral health workforce development and retention (i.e., low wages, high workloads, training gaps) are being replicated by growing efforts to expand the workforce through task-sharing delivery to nonspecialist behavioral health providers (e.g., peer specialists, promotores de salud). In this paper, we describe policy opportunities to sustain behavioral health workforce growth to meet demand while supporting fair wages, labor protections, and rigorous training.

Radiation Dose prediction for Cervical Cancer Patients Using IMRT Technique with a Machine Learning Model Based on Support Vector Regression (SVR)

Cervical cancer poses significant global health challenges, necessitating innovative treatment approaches. This study explores the integration of Support Vector Regression (SVR) in predicting radiation doses for cervical cancer radiotherapy, aiming to enhance the precision of Intensity Modulated Radiation Therapy (IMRT). We developed and validated an SVR model using datasets of 102 and 173 cervical cancer cases to predict dose distributions based on radiomic and dosiomic features. Our methodology involved pre-processing, feature extraction, data normalization, and rigorous training and testing of the model. Results indicated that larger, more diverse datasets significantly improve predictive accuracy, with the model demonstrating robust performance metrics. Notably, the study highlights the critical role of dataset size over stage uniformity in model reliability. These findings underscore the potential of machine learning in refining radiotherapy planning, reducing the workload on medical physicists, and improving patient outcomes. Future research should focus on expanding dataset sizes and enhancing model precision, particularly for challenging anatomical regions. This study paves the way for more personalized and effective radiotherapy planning, benefiting both clinicians and patients.

Penerapan Gaya Kepemimpinan Situasional dalam Menghadapi Perkembangan Teknologi di Pondok Pesantren Tarbiyah Islamiyah Gobah

Leadership within managing an Islamic educational institution must have a situation-based leadership style in the face of technological developments. So from that leadership has a challenge to technological development in the world of education. This research uses descriptive qualitative methods and its type of research is a case study. Data collection techniques include observations, interviews, documents, books, journals, and previous research. The findings of this study are that the application of situational leadership in the houses effectively overcomes the obstacles faced by education in the development of technology using versatile and adaptable strategies. Teachers receive rigorous training and engage in collaborative possibilities, producing enhanced skills and knowledge. As a result, students benefit from more participatory learning techniques, which leads to increased interest and better learning outcomes. Although there are barriers to change and constraints in technological infrastructure, a combination of policy support, external resources, and an inclusive leadership approach effectively addresses these barriers. The overall findings emphasize the importance of situational leadership in overseeing educational innovation and formulating more comprehensive and efficient policies in technological developments.

Best practice in psychoeducational assessment: beyond the manual

ABSTRACT Objective Psychologists have a responsibility to conduct standardised assessment according to instructions in test manuals and guidelines in assessment texts. In practice, circumstances arise that are not addressed in manuals and texts. The aim of this study was to explore the ways in which experienced psychologists manage some of these issues. Method The participants were 10 psychologists with postgraduate training in assessment and reputations as experts within the professional community. Years of experience ranged from 5 to more than 30 years. Most of the participants had specialist area of practice endorsement in educational and developmental psychology. Participants responded to survey questions about aspects of assessment for which ready answers could not be identified in test manuals or published texts. Each participant was asked to explain and justify their decisions. Results On some issues, participants tended to follow the way they had been taught. Other decisions were based on clinical experience, rather than on shared understandings or evidence from research. Conclusions The findings highlight the lack of empirical research about best practice in assessment, the importance of ongoing professional development, and the need for rigorous training that addresses the many issues that arise in assessment and encourages ongoing reflective practice.