Assessment Tool Research Articles

Integrating Building- and Site-Specific and Generic Fragility Curves into Seismic Risk Assessment: A PRISMA-Based Analysis of Methodologies and Applications

Fragility curves are fundamental tools in seismic risk assessments, providing insights into the vulnerability of structures to earthquake-induced damages. These curves, which plot the probability of a structure reaching or exceeding various damage states against earthquake intensity, are critical for developing effective modification strategies. This review aims to present the characteristics between building- and site-specific fragility curves, which incorporate detailed local characteristics, and generic fragility curves that apply broader, more generalized parameters. We utilize the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to systematically review the literature to address key research questions about the methodological differences, applications, and implications of these curve types in assessing seismic risks. The methods involved a comprehensive search and combination of existing studies on the topic, focusing on how these curves are developed and applied in real-world scenarios. The results from this review show that building- and site-specific curves, while more precise, require extensive data and are therefore more complex and costly to develop. In contrast, generic curves, though less accurate, offer a cost-effective solution for preliminary risk assessments over large areas. The conclusions drawn from this review suggest that while each type has its merits, the choice between building- and site-specific and generic fragility curves should be guided by the specific requirements of the seismic risk assessment task, including available resources and the need for precision in the vulnerability estimations.

The DOSE index in chronic obstructive pulmonary disease: evaluating healthcare costs

Background and objectivesThe DOSE index, which incorporates Dyspnea, Obstruction, Smoking, and Exacerbations, is a widely used tool for assessing the severity and prognosis of Chronic Obstructive Pulmonary Disease (COPD). In addition to risk assessment, it has potential clinical utility in predicting healthcare costs, which are primarily driven by exacerbations. While several indices, such as the BODE (Body-mass index, Obstruction, Dyspnea, Exercise) and ADO (Age, Dyspnea, Obstruction) indices, exist for risk prediction, there is a lack of dedicated tools for forecasting healthcare costs. This study explores the potential of the DOSE index compared to other indices, including BODE, ADO, and the Charlson Comorbidity Index (CCI), for this purpose.Materials and methodsThis cross-sectional retrospective study analyzed data from 396 COPD cases. We examined associations between the DOSE index, BODE index, ADO index, CCI, and healthcare costs, including hospitalizations and emergency room treatments. Healthcare costs were categorized as direct medical expenses.ResultsSignificant associations were observed between the DOSE index and various healthcare parameters. DOSE quartiles showed strong correlations with outpatient visits (p = 0.013) and outpatient medical expenses (p = 0.011). In addition, hospitalization frequency, duration, and associated costs were significantly correlated with higher DOSE quartiles (p < 0.001). A significant difference was found when comparing DOSE quartiles between patients with high (CCI ≥ 3) and low (CCI < 3) comorbidity scores (p = 0.018). The DOSE index outperformed other indices, likely due to its inclusion of exacerbations, a key driver of healthcare costs.ConclusionThe DOSE index demonstrates potential in predicting healthcare costs, particularly due to its inclusion of exacerbation frequency. This study highlights the importance of considering exacerbations alongside traditional risk factors for more accurate cost forecasting in COPD management. Our findings suggest that the DOSE index may be a valuable tool in both clinical and economic assessments of COPD patients, though further research is warranted to validate these findings in larger datasets.

Intervention Programs Focusing on Social Cognition in Schizophrenia

Social cognitive functions make it possible to correctly recognize and interpret the emotions and mental states of others in the social environment, to understand their intentions correctly and to react accordingly. Therefore, these functions arecritical for healthy social interactions and professional success in human life. Social cognitive functions are impaired in many psychiatric and neurological diseases and this impairment negatively affects almost all areas of patients' lives, albeit in different dimensions. According to research findings revealing the relationship between social cognition and social functioning, social cognition should be one of the treatment targets for many diseases. Recently, there has been increasing interest in interventions that target the improvement of these functions. In this article, these training programs, research findings and assessment tools used in these studies are discussed in the specific case of schizophrenia.

Validation of the Global Belief in a Just World Scale within the Spanish Population

The concept of justice, though inherently universal, is subject to substantial influence from social factors, affecting its perception. This study is dedicated to the adaptation and validation of an assessment tool for Global Belief in a Just World Scale (GBJWS) in the Spanish context. Our investigation delves into the relationship between Belief in a Just World and key constructs such as Social Dominance, System Justification, and Right-Wing Authoritarianism, while also addressing the dimension of potential gender disparities. Our findings demonstrate robust reliability coefficients, attesting to the scale's aptitude for assessing GBJW and exhibit highly favourable adjustment indices. Notably, our score analysis reveals elevated levels of BJW among men. This comprehensive research endeavour offers an invaluable global metric, facilitating transnational studies for understanding of justice perceptions on a global scale.

Nutritional assessment in the HIV-infected older population receiving antiretroviral therapy.

Malnutrition is a pronounced public health issue which often seems underestimated in the older people living with HIV (PLWH) virus infection. PLWH are highly vulnerable to nutritional problems resulting from agingrelated deterioration, disease itself, and adverse effects of antiretroviral therapy (ART). The comprehensive nutritional assessments are necessary to perform routinely in this population to monitor and provide appropriate interventions to reduce comorbid conditions. In this review, we focus on the untoward impacts of malnutrition and nutritional assessments on the morbidity and mortality in the older PLWH. Some predictive factors of nutritional status in this group of patients are discussed. We propose the important components for nutrition assessment tool for older PLWH on ART. Highlighted issue is the need for developing uniform standardized tools for the early diagnosis of malnutrition in this population. Applications of the nutritional assessments, proper nutritional interventions, and regular monitoring of nutritional status in older PLWH liv-ing in every clinical setting may help the patients get better well-being.

Generative AI detection in higher education assessments

AbstractThis chapter presents a critical analysis of generative AI (GenAI) detection tools in higher education assessments. The rapid advancement and widespread adoption of GenAI, particularly in education, necessitates a reevaluation of traditional academic integrity mechanisms. I explore the effectiveness, vulnerabilities, and ethical implications of AI detection tools in the context of preserving academic integrity. My analysis synthesizes insights from various case studies, newspaper articles, and student testimonies to scrutinize the practical and philosophical challenges associated with AI detection. I argue that reliance on detection mechanisms is misaligned with the educational landscape, where AI plays an increasing role. I advocate for a strategic shift toward robust assessment methods and educational policies that embrace GenAI usage while ensuring academic integrity and authenticity in assessments.

University-Based Coach Education: The E-Portfolio as a Formative Assessment Tool of Student-Coaches’ Learning

Current research highlights the need for more studies focused on how high-quality assessment strategies can contribute to coach learning in coach education. The use of e-portfolios, as formative assessment tools, has shown to contribute to student-coaches’ learning in university-based programs, but studies on this topic are still scarce. The aim of this action research project is to investigate the potentialities and challenges of the e-portfolio as a formative learning-oriented assessment tool in an undergraduate sport coach course in Brazil. By sharing this, from the perspective of student-coaches and the assistant-professors of the course, we reflect on the evidence with the intention to both inform colleagues doing similar work and contribute to an emerging body of assessment in coach education literature.

Real Patient Participation in Workplace-Based Assessment of Health Professional Trainees: A Scoping Review.

The aim of this scoping review is to outline the existing landscape of how real patients participate in the workplace-based assessment of trainees across diverse healthcare professions. In 2019-2020, the authors searched MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC, and Web of Science databases for studies that included descriptions of experiences whereby patients received care from a health professional trainee and participated in workplace-based assessments of that trainee. Full-text articles published in English from 2009 to 2020 were included in the search. Of the 8770 studies screened; 77 full-text articles were included. Analysis showed that strategies for patient participation in workplace-based assessment varied widely. Aspects studied ranged from validation of an assessment tool to evaluation of the impact of an educational intervention on trainees' performance. Assessment of patient satisfaction was the most common approach to patient involvement. The majority of studies were conducted in North America and in the context of physician training. Formal patient participation in the assessment of health professional trainees appears heterogeneous across health professions. Gaps in the literature are evident; therefore, this review points to an inclusive approach to workplace-based assessment to ensure patient feedback of the trainees who care for them is represented.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the "black box" nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators. We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments, enabling the identification of individual indicators' contributions to the model's predictions. We find that the transformer model outperforms others, achieving an accuracy of about 98% and an area under the receiver operating characteristic curve (AUC) of 0.891. Regionally, the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas, level IV in the northern region, and level V in the western region. Through explainability analysis, we identify that water hardness, total dissolved solids, and arsenic concentrations are the most influential indicators in the model. Our AI-driven environmental assessment model is accurate and explainable, offering actionable insights for targeted environmental management. Furthermore, this study advances the application of AI in environmental science by presenting a robust, explainable model that bridges the gap between machine learning and environmental governance, enhancing both understanding and trust in AI-assisted environmental assessments.

Prostate cancer: unmet supportive and palliative care needs: national survey of patients and family carers

ObjectivesMen living with prostate cancer have supportive and palliative needs. However, few studies detail unmet needs (vs quality of life measurement) or include data from those with advanced disease. We...

Construction and validation of a computerized creativity assessment tool with automated scoring based on deep-learning techniques.

Construction and validation of a computerized creativity assessment tool with automated scoring based on deep-learning techniques.

Advancing PFAS risk assessment: Integrative approaches using agent-based modelling and physiologically-based kinetic for environmental and health safety

Per- and polyfluoroalkyl substances (PFAS), ubiquitous in a myriad of consumer and industrial products, and depending on the doses of exposure represent a hazard to both environmental and public health, owing to their persistent, mobile, and bio accumulative properties. These substances exhibit long half-lives in humans and can induce potential immunotoxic effects at low exposure levels, sparking growing concerns. While the European Food Safety Authority (EFSA) has assessed the risk to human health related to the presence of PFAS in food, in which a reduced antibody response to vaccination in infants was considered as the most critical human health effect, a comprehensive grasp of the molecular mechanisms spearheading PFAS-induced immunotoxicity is yet to be attained. Leveraging modern computational tools, including the Agent-Based Model (ABM) Universal Immune System Simulator (UISS) and Physiologically Based Kinetic (PBK) models, a deeper insight into the complex mechanisms of PFAS was sought. The adapted UISS serves as a vital tool in chemical risk assessments, simulating the host immune system's reactions to diverse stimuli and monitoring biological entities within specific adverse health contexts. In tandem, PBK models unravelling PFAS' biokinetics within the body i.e. absorption, distribution, metabolism, and elimination, facilitating the development of time-concentration profiles from birth to 75 years at varied dosage levels, thereby enhancing UISS-TOX's predictive abilities. The integrated use of these computational frameworks shows promises in leveraging new scientific evidence to support risk assessments of PFAS. This innovative approach not only allowed to bridge existing data gaps but also unveiled complex mechanisms and the identification of unanticipated dynamics, potentially guiding more informed risk assessments, regulatory decisions, and associated risk mitigations measures for the future.

Enhancing Intraoperative Tissue Identification: Investigating a Smart Electrosurgical Knife's Functionality During Electrosurgery.

Detecting the cancerous growth margin and achieving a negative margin is one of the challenges that surgeons face during cancer procedures. A smart electrosurgical knife with integrated optical fibers has been designed previously to enable real-time use of diffuse reflectance spectroscopy for intraoperative margin assessment. In this paper, the thermal effect of the electrosurgical knife on tissue sensing is investigated. Porcine tissues and phantoms were used to investigate the performance of the smart electrosurgical knife after electrosurgery. The fat-to-water content ratio (F/W-ratio) served as the discriminative parameter for distinguishing tissues and tissue mimicking phantoms with varying fat content. The F/W-ratio of tissues and phantoms was measured with the smart electrosurgical knife before and after 14 minutes of electrosurgery. Additionally, a layered porcine tissue and phantom were sliced and measured from top to bottom with the smart electrosurgical knife. Mapping the thermal activity of the electrosurgical knife's electrode during animal tissue electrosurgery revealed temperatures exceeding 400 °C. Electrosurgery for 14 minutes had no impact on the device's accurate detection of the F/W-ratio. The smart electrosurgical knife enables real-time tissue detection and predicts the fat content of the next layer from 4 mm ahead. The design of the smart electrosurgical knife outlined in this paper demonstrates its potential utility for tissue detection during electrosurgery. In the future, the smart electrosurgical knife could be a valuable intraoperative margin assessment tool, aiding surgeons in detecting tumor borders and achieving negative margins.

Comparison of Codependency Characteristics of Individuals with and without Dependent Relatives in Terms of Defense Mechanisms, Family Functionality and Attachment Styles

Objective: This study aimed to identify both the shared and distinct aspects of the codependency characteristics of individuals with (clinical group, n=56) and without dependent relatives (non-clinical group, n=59) by comparing them in terms of personal (defense-mechanisms), domestic (family-functionality), and relational (attachment-styles) contexts.&#x0D; Method: Codependency Assessment Tool (CODAT), Defense Styles Questionnaire 40 (DSQ-40), Family Assessment Device (FAD), and Relationship Scales Questionnaire (RSQ) were used as data collection tools.&#x0D; Results: The clinical group scored statistically significantly higher on CODAT (other focus/self-neglect, low-self-worth, family-of-origin-issues subscales and total), DSQ-40 (immature-defenses subscale), FAD (failure in problem-solving, communication, role-distribution, affective-responsiveness, affective-involvement, general-functioning subscales and total), RSQ (preoccupied-attachment subscale) than the non-clinical group. The non-clinical group scored significantly higher on the mature-defenses subscale than the clinical group. Immature-defenses, failure in problem-solving and preoccupied-attachment predicted codependency in the clinical group whereas in the non-clinical group, immature-defenses and unhealthy-communication were significant predictors of codependency.&#x0D; Conclusion: Codependency characteristics differ among individuals with and without dependent family members. As immature defenses predicted codependency in both groups, the use of this defense mechanism may be a fundamental feature of codependency.

A Three-year Retrospective Study Looking at Preventing Hospital Acquired Thrombosis.

Hospital-acquired venous thromboembolism (HA-VTE) is defined as cases of venous thromboembolism (VTE) that occur in a hospital and within ninety days of a hospital admission. Deep vein thromboses (DVTs) most commonly occur within the deep veins of the pelvis and legs. If the thrombus dislodges and travels to the lungs, it can result in a pulmonary embolus (PE). VTE is associated with significant morbidity and mortality, accounting for almost 10% of all hospital deaths. If risk factors are correctly identified and VTE prophylaxis is prescribed, VTE can be a preventable condition. In 2010, NHS England launched The National Venous Thromboembolism Prevention Programme. This included NICE guidance, and a VTE risk assessment tool, which must be completed for at least 95% of patients on admission. The National Thrombosis Survey, published by Thrombosis UK, studied how this program was implemented locally, and audited HA-VTE prevention strategies nationally. Using the Thrombosis Survey and NICE guidance as an aide, this study collects data about hospital-acquired DVT (HA-DVT) at the Queen Elizabeth Hospital in Gateshead (QEH) and aims to: 1. Identify cases of HA-DVT and understand the clinical circumstances surrounding these cases 2. Assess the quality of VTE preventative measures at QEH 3. Outline potential improvement in reducing the incidence of HA-VTE at this hospital Methods: This retrospective cohort study used electronic records to identify all cases of DVT between April 2019 and April 2022 at QEH. Cases of HA-DVT were defined as: a positive ultrasound doppler report and either the case occurring in the 90 days following an inpatient stay, or beyond two days into an admission. For these cases of HA-DVT, we recorded the: reason for admission; admitting specialty; presence of an underlying active cancer and deaths occurring within 90 days of diagnosis. We assessed the quality of VTE preventative measures, by recording the: completion of VTE risk assessments; prescription of weight-adjusted pharmacological VTE prophylaxis and provision of VTE prophylaxis on discharge. For HA-DVT cases occurring within 90 days of an inpatient stay, the preventative measures were assessed on the original admission. Electronic records were used to record the completion rate of the National VTE risk assessment tool for all inpatients during this time frame. The VTE risk assessment tool was completed for 98.5% of all admissions. One hundred and thirty-five cases of HA-DVT were identified between April 2019 and April 2022. Sixteen patients with HA-DVT did not have VTE prophylaxis prescribed on admission. Eleven of these patients had a clearly documented reason why anticoagulation was avoided. In HA-DVT cases where pharmacological VTE prophylaxis was prescribed, 23% were prescribed an inappropriate dose for their weight. If anticoagulation was required on discharge, this was prescribed appropriately in 94% of cases. About 31% of the patients with HA-DVT had an underlying active malignancy. Thirty-nine patients died within 90 days of the DVT being diagnosed; in only 1 case was VTE thought to be a contributing factor to death. The hospital exceeded the national standard of VTE risk assessment completion on admission (greater than 95%). For almost a quarter of patients with HA-DVT, the dose of thromboprophylaxis prescribed was not appropriate for weight. In five cases of HA-DVT, thromboprophylaxis was omitted with no clear justification. HA-DVT often affects the most clinically vulnerable patients and is associated with a high mortality.

A review of skin cancer primary prevention activities in primary care settings.

Skin cancer is highly preventable through primary prevention activities such as avoiding ultraviolet radiation exposure during peak times and regular use of sun protection. General practitioners (GPs) and primary care nurses have key responsibilities in promoting sustained primary prevention behaviour. We aimed to review the evidence on skin cancer primary prevention activities in primary care settings, including evidence on feasibility, effectiveness, barriers and enablers. Rapid review and narrative synthesis. We searched published literature from January 2011 to October 2022 in Embase, Medline, PsychInfo, Scopus, Cochrane Central and CINAHL. The search was limited to skin cancer primary prevention activities within primary care settings, for studies or programs conducted in Australia or countries with comparable health systems. Analysis of barriers and enablers was informed by an implementation science framework. A total of 31 peer-reviewed journal articles were included in the review. We identified four main primary prevention activities: education and training programs for GPs; behavioural counselling on prevention; the use of novel risk assessment tools and provision of risk-tailored prevention strategies; and new technologies to support early detection that have accompanying primary prevention advice. Enablers to delivering skin cancer primary prevention in primary care included pairing preventive activities with early detection activities, and access to patient resources and programs that fit with existing workflows and systems. Barriers included unclear requirements for skin cancer prevention counselling, competing demands within the consultation and limited access to primary care services, especially in regional and remote areas. These findings highlight potential opportunities for improving skin cancer prevention activities in primary care. Ensuring ease of program delivery, integration with early detection and availability of resources such as risk assessment tools are enablers to encourage and increase uptake of primary prevention behaviours in primary care, for both practitioners and patients.

Efficacy of non-pharmacological interventions for primary dysmenorrhoea: a systematic review and Bayesian network meta-analysis

ObjectivesTo assess the relative benefits of various non-pharmacological interventions on treating primary dysmenorrhoea within a network meta-analysis.Study designSystematic review and Bayesian network meta-analysis.Inclusion criteriaRandomised controlled trial involving patient with primary...

Rapid reviews methods series: guidance on rapid qualitative evidence synthesis

This paper forms part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group and addresses rapid qualitative evidence syntheses (QESs), which use modified systematic, transparent and...

Application of Machine Learning Algorithms to Predict Osteoporotic Fractures in Women.

Predicting the risk of osteoporotic fractures is vital for prevention. Traditional methods such as the Fracture Risk Assessment Tool (FRAX) model use clinical factors. This study examined the predictive power of the FRAX score and machine-learning algorithms trained on FRAX parameters. We analyzed the data of 2,147 female participants from the Ansan cohort study. The FRAX parameters employed in this study included age, sex (female), height and weight, current smoking status, excessive alcohol consumption (>3 units/d of alcohol), and diagnosis of rheumatoid arthritis. Osteoporotic fracture was defined as one or more fractures of the hip, spine, or wrist during a 10-year observation period. Machine-learning algorithms, such as gradient boosting, random forest, decision tree, and logistic regression, were employed to predict osteoporotic fractures with a 70:30 training-to-test set ratio. We evaluated the area under the receiver operating characteristic curve (AUROC) scores to assess and compare the performance of these algorithms with the FRAX score. Of the 2,147 participants, 3.5% experienced osteoporotic fractures. Those with fractures were older, shorter in height, and had a higher prevalence of rheumatoid arthritis, as well as higher FRAX scores. The AUROC for the FRAX was 0.617. The machine-learning algorithms showed AUROC values of 0.662, 0.652, 0.648, and 0.637 for gradient boosting, logistic regression, decision tree, and random forest, respectively. This study highlighted the immense potential of machine-learning algorithms to improve osteoporotic fracture risk prediction in women when complete FRAX parameter information is unavailable.

Development and Validation of Instruments for Assessing the Impact of Artificial Intelligence on Students in Higher Education

&lt;p style="text-align:justify"&gt;The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.&lt;/p&gt;