Practical Assessment Tool Research Articles

Expression profiles of HMGB1 on B-CLL related leukocytes contribute to prediction of relapse

BackgroundThe High Mobility Group Box 1 (HMGB1) is a nuclear protein that is frequently overexpressed in hematologic diseases and might be of relevance in immunogenic cancer control thus correlating with patients’ (pts.) prognosis in diseases such as acute myeloid, acute lymphatic and chronic lymphocytic leukemia. Materials and methodsExpression profiles of blasts from AML (n = 21), ALL (n = 16) and of B-lymphocytes of CLL (n = 9) pts. were analyzed for surface expression of HMGB1 using flow cytometry. Expression was quantified and correlated with clinically and prognostically relevant markers. ResultsExpression profiling of HMGB1 in blasts of AML and ALL subtypes did not show differences between primary vs. secondary disease development and gender related differences. In ALL pts. however, age groups at initial diagnosis between ≥20 vs. <20 years were compared and showed significant differences (≥20 vs. <20 years; 89% vs. 49%, p <0.05) with higher expression in higher age. In AML and CLL these differences were not visible. To evaluate the prognostic significance of HMGB1 expression, expression quantity was correlated with established and prognostic classification systems (in AML ELN, in ALL GMALL) and probability to relapse. No significant correlation was seen in these entities. However, when AML pts. were analyzed for remission rates after first anthracycline based induction therapy, in those who did not experience a complete remission significantly enhanced HMGB1 surface expression was seen (98 vs. 94%; p < 0.05; n = 20). Furthermore, for CLL it was shown that higher HMGB1 expression was found in pretreated patients with relapsed or/and refractory disease (1 vs. more relapses; 94 vs. 98%; p <0.05; n = 9). ConclusionHMGB1 is frequently expressed in hematologic malignancies. In this study it was shown that HMGB1 surface expression on AML blasts can be used as predictors for treatment response. In CLL it may be a marker for advanced disease. In order to implement this marker in FACS routine it could be a useful and practical tool for prognostic assessment and treatment planning.

Four innovations: A robust integrated behavioral health program in pediatric primary care.

The increase in behavioral health problems presenting in pediatric primary care (PPC) has led to a greater focus on the prevention and treatment of mental health concerns. As a result, care has shifted from colocation to integrated PPC models. However, the literature provides limited guidance on the development and implementation of integrated PPC models that aim to transform both PPC and the larger health care system. We developed an integrated behavioral health (IBH) approach modeled with fidelity to the Integrated Practice Assessment Tool to fully integrate behavioral health into PPC. Over the 4 years since the application of our model, we have been successful in the development of an integration process and model for practice transformation of an urban PPC center and the spread and scale of IBH services to 2 additional clinics. Four elements combine to make our integrated behavioral health program novel: (a) clear mission and vision, (b) provision of universal prevention services, (c) continuous quality improvement, and (d) emphasis on practice and systems transformation. Trends in practice and scholarly inquiry indicate the need for robust pediatric IBH models, with a clear mission and vision; articulation of how research informs the development and implementation of the model; and infrastructure to meaningfully examine the model's impact. Cognizant of these needs, Cincinnati Children's Hospital Medical Center developed an IBH program. We provide key learnings for IBH program development, PPC transformation, and innovative systems redesign. We describe these elements and make recommendations for future research and practice. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Effect of Video-Assisted Structured Teaching Program on Mothers' Knowledge and Practices regarding Diarrhea Prevention among Children Less Than Five Years

Background: providing teaching programs to mothers is very important and will improve their knowledge and practice regarding diarrhea prevention among children which reduces mortality and morbidity. Aim: To evaluate the effect of video-assisted structured teaching programs on mothers' knowledge and practices regarding diarrhea prevention among children less than five years. Subjects and Methods: Design: A quasi-experimental research design was used in the current study. Setting: This study was conducted at the Medical Pediatric Outpatient Clinics at Sohag University Hospital. Sample: A convenient sample of (410) mothers was recruited in the study. Tools of data collection: Three tools were used: (I) structured interviewing questionnaire, (II) knowledge assessment tool, and (III) reported practice assessment tool. Results: The results of the present study indicated that statistically significant improvements were found in mothers' total knowledge and practice regarding diarrhea prevention among children less than five years post video-assisted structured teaching program. Conclusion: The current study concluded that video assisted structured teaching program has found to be positively affected on improving mothers' knowledge and practices level regarding diarrhea prevention among children less than five years. Recommendations: It is very important to apply a video-assisted structured teaching program for mothers of children less than five years concerning diarrhea prevention to promote and improve their knowledge and practices

Visual-Attentional Load Unveils Slowed Processing Speed in Multiple Sclerosis Patients: A Pilot Study with a Tablet-Based Videogame.

Slowing in information processing speed (IPS) is the key cognitive deficit in multiple sclerosis (MS). Testing IPS in different cognitive load conditions by using computerized tools might reveal initial IPS slowness underestimated by classic paper-and-pencil tests. To investigate the extent to which IPS can be affected by increased task demands, we developed three tasks based on the manipulation of the visual-attentional load, delivered with a home-made, tablet-based videogame. Fifty-one patients with MS (pwMS), classified as having no cognitive impairment in classic paper-and-pencil tests, and 20 healthy controls (HC) underwent the videogame tasks; reaction times (RTs) and accuracy were recorded. A significant reduced performance of pwMS as compared with HC was found on the videogame tasks, with pwMS being on average slower and less accurate than HC. Furthermore, pwMS showed a significantly more pronounced decrement in accuracy as a function of the visual-attentional load, suggesting a higher susceptibility to increased task demands. Significant correlations among the Symbol Digit Modalities Test (SDMT) and the videogame mean RTs and accuracy were found, providing evidence for the concurrent validity of the videogame as a valid tool to test IPS in pwMS. The high potential that might derive from the adoption of computerized assessment tools in clinical practice should be taken into consideration and investigated further.

Rapid Quality Control of Woodchip Parameters Using a Hand-Held Near Infrared Spectrophotometer

Near infrared spectroscopy is a non-invasive and rapid technique to support the analysis of solid biofuels such as woodchip, which is considered as a suitable alternative for energy production, according to European goals for fossil fuel reduction. Chemical and physical properties of the woodchip influence combustion performance, so the most discriminant parameters such as moisture and ash content and gross calorific value were constantly monitored. The aim of this study was the development of prediction models for these three parameters with the use of a hand-held NIR spectrometer. Laboratory analyses were carried out to evaluate the quality of several Italian samples from a power plant, and PLS regression models were developed to test prediction accuracy. Moreover, the most relevant wavelengths were investigated to discriminate chemical compounds influence. Prediction models demonstrated the capacity of handheld MicroNIR instrument to be considered a practical tool for solid biofuel quality assessment. As a consequence, NIR spectroscopy improved real-time analysis and made it suitable for practical and industrial applications, as supported by the recent Italian standard UNI/TS 11765.

Evidence-Based Assessment in Routine Mental Health Services for Youths.

Evidence-based assessment (EBA) plays a critical role in the delivery of effective treatments. However, little is known about the assessment practices of mental health clinicians who treat youths and the factors that support EBA. The authors examined when, how, and under what conditions clinicians conduct EBA. In two multidisciplinary surveys (combined N=2,575), clinicians reported how frequently they conducted pretreatment, ongoing, and posttreatment assessments and how frequently they used standardized measures in usual care of youths. Although clinicians reported frequent pretreatment, ongoing, and posttreatment assessments, use of standardized measures was rare. Clinician and practice setting characteristics predicted standardized measure use, and a lack of practical assessment tools appears to be a barrier to use of standardized measures in EBA of youths. Many clinicians conduct assessments during treatment, but more practical measures and clinician training may improve the integration of standardized measures into routine practice.

Improving injection safety practices of Cambodian healthcare workers through training.

BackgroundThis study evaluated the impact of a safe injection safety training on healthcare worker (HCW) practice and knowledge following an HIV outbreak in Roka commune, Cambodia.MethodsSurveys were conducted at baseline (September 2016) and seven months after a training intervention (March 2018) using the World Health Organization standardized injection practices assessment tool. HCWs were sampled at 15 purposively government health facilities in two provinces. HCWs were observed during injection practices and interviewed by trained experts from Becton-Dickinson and the Ministry of Health Cambodia. The Rao-Scott chi square test was used test for differences between baseline and follow-up.ResultsWe completed 115 observations of practice at baseline and 206 at post-training follow-up. The proportion of patients whose identification was confirmed by HCWs prior to procedure being performed increased from 40.4% to 98% (p <0.0001). The proportion of HCWs who practiced correct hand hygiene increased from 22.0% to 80.6% (p = 0.056) [therapeutic observations] and 17.2% to 63.4% (p = 0.0012) [diagnostic observations]. Immediate disposal of sharps by HCWs decreased from 96.5% to 92.5% (p = 0.0030).ConclusionsWe found significant improvements in the practice of patient identity confirmation and hand hygiene but not in the immediate disposal of sharps in the post-training intervention. However, findings are not representative of all HCWs in the country. Further pre-service and in-service training and monitoring are necessary to ensure sustained behavior change.

Designing individual-specific and trial-specific models to accurately predict the intensity of nociceptive pain from single-trial fMRI responses

Using machine learning to predict the intensity of pain from fMRI has attracted rapidly increasing interests. However, due to remarkable inter- and intra-individual variabilities in pain responses, the performance of existing fMRI-based pain prediction models is far from satisfactory. The present study proposed a new approach which can design a prediction model specific to each individual or each experimental trial so that the specific model can achieve more accurate prediction of the intensity of nociceptive pain from single-trial fMRI responses. More precisely, the new approach uses a supervised k-means method on nociceptive-evoked fMRI responses to cluster individuals or trials into a set of subgroups, each of which has similar and consistent fMRI activation patterns. Then, for a new test individual/trial, the proposed approach chooses one subgroup of individuals/trials, which has the closest fMRI patterns to the test individual/trial, as training samples to train an individual-specific or a trial-specific pain prediction model. The new approach was tested on a nociceptive-evoked fMRI dataset and achieved significantly higher prediction accuracy than conventional non-specific models, which used all available training samples to train a model. The generalizability of the proposed approach is further validated by training specific models on one dataset and testing these models on an independent new dataset. This proposed individual-specific and trial-specific pain prediction approach has the potential to be used for the development of individualized and precise pain assessment tools in clinical practice.

Identifying challenges in promoting adolescent preventive health care in the primary care setting of urban Japan.

Despite the assurance of free medical access through the universal health insurance system, over the past three decades Japanese youth have experienced an increase in developmental and behavioral problems, psychosomatic disorders, and psychiatric illnesses. To identify the challenges in promoting comprehensive preventive health services to adolescents in Japan, specifically in Tokyo. A cross-sectional online survey was conducted of physicians who are members of the Tokyo Pediatric Association. The questionnaire assessed primary care pediatricians' practice settings and their attitudes toward outpatient services to adolescent patients. Out of 617 member pediatricians, 69 valid responses were obtained. Majority were private pediatricians. 52% indicated that they had specialties in addition to general pediatrics; however, no one reported specializing in adolescent medicine. Approximately 70% answered the optimal patient encounter time would be under 10min. More than 90% of respondents reported challenges providing health services to adolescent patients. Nearly half indicated that they did not routinely evaluate height, heart rate or blood pressure. Few providers asked about reproductive health, violence, or smoking/alcohol use. This survey is the first exploration of adolescent health care in an urban area of Japan. While the sample is small, the magnitude of challenges was great with limited training in adolescent medicine, severe time constraints and limited appreciation for the value of health screening at each encounter. Developing a standardized practical assessment tool for adolescent patients may help guide primary care pediatricians to better meet the needs of their adolescent patients.

A novel tool for automated assessment of listener transcripts in speech intelligibility studies

In the field of speech perception, many studies assess the intelligibility of spoken stimuli by means of verbal repetition (“repeat back what you hear”) or transcription tasks (“type out what you hear”). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target stimulus. Yet scoring the participants’ raw responses for words correctly identified from the target stimulus is a time-consuming task, and hence resource-intensive. Moreover, there is no consensus on what protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of “fuzzy string matching” between participants’ responses and target sentences as automated metrics of listener transcript accuracy. Fuzzy string matching is identified as a consistent, efficient, and accurate method for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (r = 0.922) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for assessment of listener transcript accuracy in large-scale speech intelligibility studies.

Performance in Practice: Practice Assessment Tool for the Care of Patients With Schizophrenia.

Schizophrenia is associated with significant health, social, occupational, and economic burdens, including increased mortality. Despite extensive and robust research on the treatment of individuals with schizophrenia, many individuals with the illness do not currently receive evidence-based pharmacological and nonpharmacological treatments. The American Psychiatric Association Practice Guideline for the Treatment of Patients With Schizophrenia, Third Edition, aims to enhance knowledge and increase the appropriate use of interventions for schizophrenia, thereby improving the quality of care and treatment outcomes. To this end, this evidence-based Performance in Practice tool can facilitate the implementation of a systematic approach to practice improvement for the care of individuals with schizophrenia. This practice assessment activity can also be used in partial fulfillment of Continuing Medical Education and Maintenance of Certification, part IV, requirements, which can also satisfy requirements for the Centers for Medicare & Medicaid Services Merit-based Incentive Payment System program.

Development of A Practical Heart Failure Readmission Risk Assessment Tool

Introduction Heart failure (HF) risk prediction models have been developed to predict 30 day all-cause hospital readmissions; however, it is difficult to use these models clinically due to limitations of the electronic health record (EHR). For example, ejection fraction may not always be readily available as a discrete field if it is reported in ECHO text. Additionally, existing models have not assessed guideline-directed medical therapy (GDMT). Objectives The objectives of this study were to evaluate admission risk factors associated with 30-day readmission for patients with HF and to design a risk prediction model for readmission risk factors that can be incorporated into the EHR. Methods This is a retrospective cohort study evaluating HF patients admitted between August 1, 2015 and June 30, 2019. Reports of HF admissions and readmissions were obtained and randomized with a goal of having 300 patients in each cohort to compare and contrast demographic variables and evaluate risk factors associated with readmission. A univariate analysis and a backwards stepwise analysis of variables in the literature were performed to evaluate potentially significant variables in a multivariate regression model. Variables achieving significance in the multivariate regression were included in the risk score. Results There were 693 patients screened for inclusion and 601 patients were ultimately included (301 readmitted and 300 not readmitted). Risk factors on admission found to be significantly associated with readmission included atrial fibrillation/flutter (p=0.042), diagnosis of chronic kidney disease (CKD) (p Conclusions Chronic kidney disease, hydralazine/isosorbide, systolic blood pressure, and Medicare were found to independently predict 30-day readmissions and were included in the final risk score. The risk score includes discrete variables and fields that can be extracted from the EHR and automatically calculated for clinical utilization. The utility of this risk score makes it unique because it focuses on discrete variables and having this calculated upon hospital admission presents an opportunity to identify and intervene on high risk patients early.

Condition Assessment of Civil Structures for Structural Health Monitoring Using Supervised Learning Classification Methods

Structural health monitoring is an essential process for ensuring the safety and serviceability of civil structures. When a structure suffers from damage, it is necessary to implement maintenance programs for returning the structural performance and integrity to its initial normal condition. An important challenge is that the structure of interest may be damaged even after a sophisticated maintenance program. This conveys the great necessity of performing the second level of structural condition assessment and damage detection of maintained structures. To achieve this aim, this article proposes a novel methodology using the concept of supervised learning. The main objective of the proposed methodology is to train various supervised learning classifiers using a training dataset that consists of features regarding both the undamaged and damaged states of the structure before the maintenance program in the first level. Once the classifiers have been trained, one attempts to predict the class labels of test samples associated with the current state of the structure after the maintenance program during the second level. According to the predefined class labels of the training and test samples in the first stage, it is feasible to recognize the current state of the maintained structure in the second level and detect potential damage. The major contribution of this article is to introduce the concept of supervised learning for damage detection in an innovative manner. A numerical concrete beam and an experimental laboratory frame are used to demonstrate the effectiveness and applicability of the proposed methodology. Results show that this methodology is a practical and reliable tool for structural condition assessment and damage detection of maintained structures.

Comparison of Group Discussion and Teach Back Selfcare Education Effects on Knowledge, Attitude, and Performance of Hemodialysis Patients

Background: Hemodialysis patients require self-care training to manage their problems. To understand the effectiveness of different educational methods, it is necessary to evaluate these methods using knowledge, attitude, and practice assessment tool. Objectives: The current study aimed to compare the effects of group discussion and teach-back self-care education on the knowledge, attitude, and performance of hemodialysis patients. Methods: This quasi-experimental study is conducted on 67 patients who were undergoing hemodialysis via fistula for at least 6 months. Using the convenient sampling method, the patients were randomly allocated into two groups: teach-back training (n = 34), and group discussion (n = 33). Both groups were provided with three sessions of self-care training on nutrition, activity, and fistula care by the researcher. Considering the teaching materials, patients in the teach-back group were taught individually to ensure complete comprehension of the information. On the other hand, subjects in the group discussion were exposed to the teaching materials in the form of group training. Data were collected before and one month after providing the training sessions. Results: In this study, the majority of patients in both groups were married men with an average age of 52 years. Following education, knowledge, attitude, and performance increased in both groups. However, the teach-back method (113.88 ± 4.13) had a higher impact on the attitude of patients than group discussion (110.48 ± 5.68) (P = 0.009). Conclusions: Teach-back education increased the knowledge, performance, and attitude of patients. It is, therefore, recommended to use this method for patients with negative attitudes toward treatment and disease.

Positive psychology in health care: defining key stakeholders and their roles.

Lifestyle-related diseases have common risk factors: physical inactivity, poor diet, inadequate sleep, high stress, substance use, and social isolation. Evidence is mounting for the benefits of incorporating effective methods that promote healthy lifestyle habits into routine health care treatments. Research has established that healthy habits foster psychological and physiological health and that emotional well-being is central to achieving total well-being. The Happiness Science and Positive Health Committee of the American College of Lifestyle Medicine aims to raise awareness about strategies for prioritizing emotional well-being. The Committee advocates for collaborative translational research to adapt the positive psychology and behavioral medicine evidence base into methodologies that address emotional well-being in nonmental health care settings. Another aim is to promote health system changes that integrate evidence-based positive-psychology interventions into health maintenance and treatment plans. Also, the Committee seeks to ameliorate health provider burnout through the application of positive psychology methods for providers' personal health. The American College of Lifestyle Medicine and Dell Medical School held an inaugural Summit on Happiness Science in Health Care in May 2018. The Summit participants recommended research, policy, and practice innovations to promote total well-being via lifestyle changes that bolster emotional well-being. These recommendations urge stakeholder collaboration to facilitate translational research for health care settings and to standardize terms, measures, and clinical approaches for implementing positive psychology interventions. Sample aims of joint collaboration include developing evidence-based, practical, low-cost behavioral and emotional assessment and monitoring tools; grants to encourage dissemination of pilot initiatives; medical record dashboards with emotional well-being and related aspects of mental health as vital signs; clinical best practices for health care teams; and automated behavioral programs to extend clinician time. However, a few simple steps for prioritizing emotional well-being can be implemented by stakeholders in the near-term.

Development of a Web-Based Tool for Risk Assessment and Exposure Control Planning of Silica-Producing Tasks in the Construction Sector.

We describe the development and implementation of a novel, on-line risk assessment tool for respirable crystalline silica (RCS) exposure for use in the construction sector. It was motivated by the introduction of new OHS regulation in British Columbia that allowed for the substitution of exposure measurement data with “objective air monitoring data” collected at “equivalent work operations.” This allowance encouraged the introduction of quantitative risk assessment in a notoriously challenging work environment but it was concluded that without assistance, the typical construction employer would struggle to identify, extract, and interpret validate objective data. The tool described here was based on a continually-updatable RCS exposure database, and a predictive regression model based on the database to generate exposure risk estimates. The model was embedded in an adaptive web-based application that can be run on common platforms. The design followed standard web conventions and features so that no specialized training is required for its use. It was designed to be usable by end-users with varying expertise, including non-OHS experts. Users describe the RCS-dust generating task they will perform, and associated control measures. The tool estimates both uncontrolled and controlled task-based exposure concentrations. Using additional information entered by the user, the on-line tool generates an “exposure control plan” or ECP, a legally regulated document for those undertaking work potentially exposing workers to RCS particulate. The development of the tool was a community-based, tri-partite effort of the local OHS regulator, construction employers, and exposure scientists. In addition to being a practical risk assessment tool, the designers wanted it to function as an educational tool, and that it should explore novel methods for exposure data collection and use. The strengths of this approach include the publicly shared updateable database that encourages continuous improvement and illustrates best practices; and the timely and cost effective collection and sharing of exposure data in a value-added manner. It is however limited to a single task per ECP, and only considers exposure to task operators, and not adjacent workers. Currently in BC, users generate up to 3,900 ECP's per year with the tool.

Ground influence on high-pressure methane jets: Practical tools for risk assessment

High-pressure gaseous methane release is a relevant safety-related problem mainly in the Oil and Gas industry. As well documented, the reason for these safety concerns is connected with the severe consequences of the domino effect subsequent to the possible ignition. In risk assessment activities, estimation of the damage area is of primary importance in order to draw up proper safety guidelines. To do this, loss prevention specialists use quick and well-established numerical tools (i.e., integral models) in their daily activities. However, the presence of an obstacle in the flow field of the jet (e.g., the ground) is a more probable situation to deal with. It is known that integral models fail in this kind of scenario, leading to unreliable predictions. Hence, the present work investigates how an industrial ground surface influences the LFL cloud size of a horizontal high-pressure methane jet. An innovative quick procedure is proposed allowing to determine the height below which the ground begins to influence the LFL cloud size and the extent of such influence. Therefore, this procedure allows practitioners to establish when integral models can be used and when not to use them, and also provides a simple and reliable alternative to their use. These analytical instruments are derived from an extensive computational fluid dynamics analysis performed with Ansys Fluent 19.0.

How Good Are Child Vulnerability Assessment Tools in China?

Accurately assessing children’s vulnerabilities and needs is important for child service delivery in social work. In China, different agencies (including international non-governmental organizations, government sectors and social organizations) utilize different tools for assessing children’s needs. However, the advantages and disadvantages of these tools have been rarely discussed, leaving their quality and effectiveness unknown. As a result, it is difficult to realize the goal of improving the well-being of Chinese children. This paper contributes to existing knowledge through the use of semi-structured interviews with key stakeholders of different types of agencies, along with an in-depth exploration, analysis and comparison of their practical assessment tools.

Interpreting Frame Transformations in AC Systems as Diagonalization of Harmonic Transfer Functions

Analysis of ac electrical systems can be performed via frame transformations in the time-domain or via harmonic transfer functions (HTFs) in the frequency-domain. The two approaches each have unique advantages but are hard to reconcile because the coupling effect in the frequency-domain leads to infinite dimensional HTF matrices that need to be truncated. This paper explores the relation between the two representations and shows that applying a similarity transformation to an HTF matrix creates a direct equivalence to a frame transformation on the input-output signals. Under certain conditions, such similarity transformations have a diagonalizing effect which, essentially, reduces the HTF matrix order from infinity to two or one, making the matrix tractable mathematically without truncation or approximation. This theory is applied to a droop-controlled voltage source inverter as an illustrative example. A stability criterion is derived in the frequency-domain which agrees with the conventional state-space model but offers greater insights into the mechanism of instability in terms of the negative damping (non-passivity) under droop control. The paper not only establishes a unified view in theory but also offers an effective practical tool for stability assessment.

An interval type-2 fuzzy reasoning model for digital transformation project risk assessment

This study presents a new risk assessment model by combining interval type-2 fuzzy best-worst method (IT2F-BWM) and perceptual reasoning for risk evaluation of digital transformation projects. Digital transformations are technology-driven change processes that transform many businesses. Digital transformations are risky and difficult undertakings so that the decision-makers should be supported with appropriate risk assessment tools. The proposed model is a step towards this direction by providing decision-makers with a practical and applicable risk assessment tool that computes with words through a transparent and interpretable reasoning mechanism. In the proposed model, the relative importance of risk factors (RFs) is derived by using IT2F-BWM. Then, IT2F rule-based model is developed by considering risk factors along with the likelihood and severity of risks. The risk magnitudes of digital transformation projects are evaluated via perceptual reasoning. Both the input and output of the reasoning model are IT2F numbers, and the resulting risk magnitudes are decoded into words such as critical, major, and minor so as to ease interpretation. A real-life digital transformation risk assessment case study is conducted in order to demonstrate the applicability of the proposed model.