A study evaluating the effect of Educational Needs Assessment Tool (ENAT) focused patient education, on health outcomes in patients with Rheumatoid Arthritis: Protocol of an ongoing study

Aims and objectives: To evaluate the effectiveness of a patient education tool (the ENAT) on self efficacy, patient knowledge, health outcomes and its usability in practice. Background: The effectiveness and value of patient education in rheumatoid arthritis have been questioned. The ENAT was developed to help the nurses to provide more focused, individualized and relevant education. The psychometric properties of the ENAT have been assessed. Design: This study is a single blind randomized controlled trial using both quantitative and qualitative methodology. The primary outcome is patient self efficacy. Secondary outcomes are physical function, psychological status, pain levels, social interaction and patient knowledge of disease. Methods: 130 RA patients will be randomized into an experimental (EG) or control group (CG). The EG patients will complete the ENAT prior to seeing a Clinical Nurse Specialist (CNS) at weeks 0, 16 and 32. The CNS will use the ENAT as a template to meet patients perceived educational needs in addition to the usual care. The CG will receive their usual care from the CNS without the aid of the ENAT. To determine the ENAT’s usability, semi-structured quailtative interviews will take place with the practitioners and a sample of patients once they have completed the quantitative arm of the study. Results: The study commenced in April 2011 and the results are expected to be available after April 2013. Conclusions: It is hypothesized that nurses’ use of the ENAT will lead to increased patient self efficacy, and improved patient knowledge and health outcomes among the patients who used it. Furthermore, it is hypothesized that the ENAT will be easy for staff and patients to use. Relevance to clinical practice: The ENAT was developed to assess the perceived educational needs of patients with rheumatoid arthritis and thus help the nurses to provide more focused, individualised and relevant education.

Assessment of Educational Videos on the Prevention and Management of Infant Diarrhea:Documental Study

Objective: To evaluate the suitability of educational videos produced in Brazil that addressed aspects related to prevention and/or management of infant diarrhea according to content;language;graphic illustrations;stimulation for learning/motivation and cultural fit. Method: Documentary study, which evaluated six educational videos from an adapted and validated tool, the Suitability Assessment of Materials. Three nurses evaluated the videos. Data were processed using the Statistical Package for the Social Sciences, version 17.0, and presented in tables and graphics. Results: It was found that 4 (66.6%) of the educational videos were classified as appropriate, ranging from 60% to 63.3% of full approval, and 2 (33.3%) were assessed as higher, reaching up to 83.3% to 90% approval. Considering the socioeconomic and cultural contexts of the videos to the audience, they assigned notes ranging from 6 to 9 in a rating ranging from 0, the minimum score, to 10, the highest score. Conclusion: The educational videos were considered adequate, with positive results about the content on the prevention and management of infant diarrhea, being appropriate for the target audience. As a result, greater credibility is ratified as the use thereof, since these resources can contribute to health promotion and prevention of diarrhea in children.

Impact of Educational Programs about Methods of Assessment of Fetal Wellbeing during Pregnancy among Staff Nurses

Objective: Aims of the study are to study the impact of increased knowledge and awareness among staff nurses towards assessment of methods of fetal wellbeing to early identification of pregnancy outcomes and the frequency of interventions during delivery. Design: A quasi-experimental design was used in carrying out the study to impact knowledge and awareness assessment of methods of fetal wellbeing among staff nurses. The study was conducted at obstetric & gynecological department working at setting in Jarsh Governorate Hospitals & Prince Hussein Bin Abdullah Hospitals. Methods: The study sample consisted of all staff nurses working at obstetric & gynecological department in Jarsh Governorate Hospital & Prince Hussein Bin Abdullah Hospitals who have agreed to participate in the study. The current study has included 150 staff types of sample convenient sample. Results: The main findings of this study showed that there is a highly statistical significance differences between pre-test and post-test in all answers regarding knowledge about methods of fetal wellbeing assessment. Conclusion: Nurses have a major role to prevent the complication for the fetus and mother during pregnancy. Nurses coming in contact with the antenatal mothers should take initiative to provide necessary information to the women and the relatives on different methods used for the assessment of fetal wellbeing, so as to improve the quality of life among the pregnant women. For that they should adequate knowledge about all the aspects of fetal wellbeing, so that they can prevent the complication.

The Construction of a Learning-Oriented Speaking Assessment Model in College English Based on the Online Diagnostic Assessment

Since language is a tool for communication,proficiency in English communication is a fundamental necessity for talent in the 21st century.However,surveys reveal that most college students at private colleges possess inadequate oral English skills,and what some have learned is“mute English.”Therefore,developing their English-speaking skills is another challenge faced by students attending private schools.Online diagnostic assessment methods are growing globally with the use of technology.UDig diagnostic assessment system is one of the online English diagnostic assessment platforms currently being widely used in China.Therefore,the present work is conducted to investigate and conduct an oral English learning-oriented assessment model in college English using the online diagnostic assessment.With the research result,it is hoped that the study could provide useful information for improving UDig system and make a better use of it in college oral English learning and teaching.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

Environmental Impact Assessment of Onshore Wind Farms in the Region of Central Greece Using a Modified RIAM Method

Wind energy is one of the most basic forms of renewable energy,which shows an increasing rate of development worldwide and also at the European level.However,this rapid deployment of wind farms makes the need for an impact assessment of this type of projects on the natural and man-made environment imperative.The present paper aims to identify and assess the environmental impacts of wind farm projects in the Region of Central Greece.A modified Rapid Impact Assessment Matrix(RIAM)method is used for this purpose.The methodology includes the identification of the existing onshore wind farm projects in the study area,the appropriate modifications of the RIAM method to respond to the characteristics of the projects and the study area,the qualitative assessment of their potential impacts during construction and operational phases and the computation of the Environmental Performance Grade(EPG)of projects based on the pro-posed modified RIAM method.The results reveal that although there are some slight negative impacts on the natural environment of the study area,the examined wind farms contribute positively both to the atmosphere and to the socio-economic environment of the study.This study extends the potential for using RIAM as a tool in environmental impact assessment studies of renewable energy projects.

Marketization of Education in China:An Assessment of its Impact on Educational Institutions

本文主要从优点和缺点两方面分析现代化、市场化对中国教育体制造成影响的和新产生的问题,并提出解决问题的政策思路。

Preventing Pregnant Women’s Exposure to Secondhand Smoke: Development and Suitability Assessment of an Educational Comic Booklet

Aim: The aim of this mixed methods research was to develop an educational comic booklet to prevent pregnant women’s exposure to secondhand smoke. Methods: We assessed the suitability of the comic booklet by measuring participant response to content, literacy demand, graphics, layout and typography, learning stimulation, motivation, and cultural appropriateness. The participants were 17 Indonesians living in Japan who were recruited through Respondent-Driven-Sampling and met all criteria for the survey. Means and standard deviations were used to determine the suitability of the educational comic. Results: About 80% of participants rated the comic as “superior” on a rating scale with options of “superior”, “adequate”, “not suitable”, or “not applicable”. The most successful aspects of the comic were content and cultural appropriateness, as it provided clear contents and the graphics showed realistic Indonesian smoking behavior. The least successful aspect of the comic was the literacy demand because there were long sentences using difficult words. Conclusions: The results of this study may be used to conduct a randomized controlled trial using this comic booklet with some modifications.

A new approach to classroom-based language assessment

If one considers the number of language learners that are worldwide,the number of classroom-based language assessments(CBLAs)that are given each year,and the number of decisions that are made on the basis of these,it is obvious that in terms of sheer numbers,more students are affected by CBLAs per year than by those based on large-scale language assessments.Because of this,it is essential that classroom teachers have the knowledge,skills,and tools to enable them to develop and use CBLAs that they can justify to stakeholders,e.g.,students,parents,and school authorities.In this paper we describe the approach to CBLAs that we have developed.First,we discuss the role of assessment in teaching and learning,the kinds of decisions that classroom teachers need to make,and the different modes of CBLAs.We then describe the process of using CBLAs to help teachers make decisions that will have beneficial consequences.Next,we discuss fairness and accountability in assessment and the process of assessment justification,including an assessment use argument.Finally,we discuss the process of developing CBLAs,using an example of a classroom-based language assessment to illustrate this.

Lead, Zinc and Iron Pollutants Load Assessment in Selected Rivers in Southern Nigeria: Implications for Domestic Uses

The aim of the study is to comparatively assess the concentrations of lead, zinc and iron in Rivers Ase, Warri and Ethiope, in Nigeria. Monthly water samples were collected from six randomly selected sites along the rivers course. 72 water samples were collected from each river at 0 - 15 cm depths. Samples were analysed based on the standard methods recommended by the WHO for testing lead, zinc and iron. The assessment of the water quality was done using the Water Quality Index (WQI) of the Canadian Council of Ministers of the Environment (CCME-WQI). While hypotheses were tested using ANOVA. Findings indicated that CCME-WQI values were 47.3, 66.52 and 78.7. This meant that the water quality of River Ase is impaired and departed from desirable levels, while that of Warri and Ethiope were considered to occasionally be impaired and depart from desirable levels. The ANOVA model showed that there is a significant variation in heavy metal load in the selected rivers at P < 0.05. River water was put to domestic uses such as drinking (20.5%) preparing food (17.8%), bathing (19.8%), washing clothes and dishes (21.3%), brushing teeth (13.3%), and catering for domestic animals (7.5%). Poverty (49.5%) was the major reason for the use of river water for domestic purposes. The locals highlighted that they usually suffer from cholera (26.8%), diarrhoea (25.8%), dysentery (24%) and typhoid (23.5%) as a result of using the river water. The study recommended routine monitoring of anthropogenic and geologic activities, testing of the water regularly amongst others.

Nutritional Assessment Tools for Patients with Cancer:A Narrative Review

Cancer patients are at high risk of malnutrition,which can lead to adverse health outcomes such as prolonged hospitalization,increased complications,and increased mortality.Accurate and timely nutritional assessment plays a critical role in effectively managing malnutrition in these patients.However,while many tools exist to assess malnutrition,there is no universally accepted standard.Although different tools have their own strengths and limitations,there is a lack of narrative reviews on nutritional assessment tools for cancer patients.To address this knowledge gap,we conducted a non-systematic literature search using PubMed,Embase,Web of Science,and the Cochrane Library from their inception until May 2023.A total of 90 studies met our selection criteria and were included in our narrative review.We evaluated the applications,strengths,and limitations of 4 commonly used nutritional assessment tools for cancer patients:the Subjective Global Assessment(SGA),Patient-Generated Subjective Global Assessment(PG-SGA),Mini Nutritional Assessment(MNA),and Global Leadership Initiative on Malnutrition(GLIM).Our findings revealed that malnutrition was associated with adverse health outcomes.Each of these 4 tools has its applications,strengths,and limitations.Our findings provide medical staff with a foundation for choosing the optimal tool to rapidly and accurately assess malnutrition in cancer patients.It is essential for medical staff to be familiar with these common tools to ensure effective nutritional management of cancer patients.

Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of GIM products in data-sparse regions is of paramount importance.In this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in 2013-2014.The indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM products.Results revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error variations.These results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.

Distribution, health and ecological risk assessments of trace elements in Nigerian oil sands

The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.

Assessment of runoff changes in the sub-basin of the upper reaches of the Yangtze River basin, China based on multiple methods

Quantitative assessment of the impact of climate variability and human activities on runoff plays a pivotal role in water resource management and maintaining ecosystem integrity.This study considered six sub-basins in the upper reaches of the Yangtze River basin,China,to reveal the trend of the runoff evolution and clarify the driving factors of the changes during 1956–2020.Linear regression,Mann-Kendall test,and sliding t-test were used to study the trend of the hydrometeorological elements,while cumulative distance level and ordered clustering methods were applied to identify mutation points.The contributions of climate change and human disturbance to runoff changes were quantitatively assessed using three methods,i.e.,the rainfall-runoff relationship method,slope variation method,and variable infiltration capacity(Budyko)hypothesis method.Then,the availability and stability of the three methods were compared.The results showed that the runoff in the upper reaches of the Yangtze River basin exhibited a decreasing trend from 1956 to 2020,with an abrupt change in 1985.For attribution analysis,the runoff series could be divided into two phases,i.e.,1961–1985(baseline period)and 1986–2020(changing period);and it was found that the rainfall-runoff relationship method with precipitation as the representative of climate factors had limited usability compared with the other two methods,while the slope variation and Budyko hypothesis methods had highly consistent results.Different factors showed different effects in the sub-basins of the upper reaches of the Yangtze River basin.Moreover,human disturbance was the main factor that contributed to the runoff changes,accounting for 53.0%–82.0%;and the contribution of climate factors to the runoff change was 17.0%–47.0%,making it the secondary factor,in which precipitation was the most representative climate factor.These results provide insights into how climate and anthropogenic changes synergistically influence the runoff of the upper reaches of the Yangtze River basin.

Assessment of Breast Cancer Prevention Practices among Women Attending Primary Health Care in Abha City, Aseer Region, Saudi Arabia

Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In Saudi Arabia, breast cancer ranked the first among females in 2014, accounting for 15.9% of all cancers reported among Saudi nationals and 28.7% of all cancers reported among females of all ages. Early detection of breast cancer could decrease the risks, have a better prognosis, and have better outcomes/more successful treatments. Prevalence of breast cancer reached more than 25% of all diagnosed cancer in the kingdom among women. Aim: This study aims to assess the knowledge and performance of women attending primary care centers about breast self-examination and mammogram screening for prevention and early detection of breast cancer in Abha city primary healthcare centers, Kingdom of Saudi Arabia. Research Method: cross sectional design was conducted by using questionnaire, which was distributed to primary care center nurses. The collected data was statistically analyzed using the Statistical Package for Social Sciences, version 25. Results: The study found that participants had poor awareness and knowledge about breast self-examination, risk factors for breast cancer, and trends and practices in early diagnosis of breast cancer. Conclusion and Recommendations: It recommends increasing awareness campaigns and providing educational programs to improve knowledge and practices.

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

The Role of Abdominal CT in the Assessment of the Effectiveness of Gastric Lavage after Pesticide Poisoning

Pesticide poisoning is one of the most common diseases in the emergency department, characterized by rapid changes in condition, a high misdiagnosis rate, and a poor prognosis. Measures for early removal of poisons are crucial, and gastric lavage is one of the important measures. Regarding the post-gastric lavage effect, abdominal CT scanning has an important application value in the assessment of the gastric lavage effect after pesticide poisoning.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.