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The research presented in this article is a systematic review of the literature on the assessment of mathematical modeling in the setting of mathematics education published in the previous five years. This research has compiled the current best information from around the world to offer an overview of the assessment of mathematical modeling for pre-service mathematics teachers or mathematics teachers. We followed the approach used in Joklitschke et al. (2021), which involved 10 steps in systematic literature review (SLR). We accessed using Web of Science (WoS), Scopus, and mathematics education journals that are ranked 1-10. Based on a full-text analysis of 18 peer-reviewed papers published in English, most of the research was conducted among pre-service mathematics teacher and most of the studies were conducted in Turkey, United States, and Germany. The future trends and opportunities were also discussed. We also found that most test types employed for measuring modeling competency were project, cognitive dimension, holistic approach, and the tests utilized more qualitative approach.

In 2020, Year 12 students at John Curtin College of the Arts, were required to model COVID-19 data from five different countries in order to find correlations between daily infections and unemployment rates, in order to make future predictions. Work received from students demonstrated how the task successfully provided unique learning opportunities, which are otherwise not experienced in a classroom environment under the pressures of a curriculum that is highly exam-focused and based on mechanistic algorithms. The paper also discusses some challenges faced and how they were overcome.

Background: Despite disproportionate rates of mental ill-health compared with non-Indigenous populations, few programs have been tailored to the unique health, social, and cultural needs and preferences of young Aboriginal and Torres Strait Islander males. This paper describes the process of culturally adapting the US-based Young Black Men, Masculinities, and Mental Health (YBMen) Project to suit the needs, preferences, culture, and circumstances of Aboriginal and Torres Strait Islander males aged 16–25 years in the Northern Territory, Australia. YBMen is an evidence-based social media-based education and support program designed to promote mental health, expand understandings of gender and cultural identities, and enhance social support in college-aged Black men. Methods: Our adaptation followed an Extended Stages of Cultural Adaptation model. First, we established a rationale for adaptation that included assessing the appropriateness of YBMen's core components for the target population. We then investigated important and appropriate models to underpin the adapted program and conducted a non-linear, iterative process of gathering information from key sources, including young Aboriginal and Torres Strait Islander males, to inform program curriculum and delivery. Results: To maintain program fidelity, we retained the core curriculum components of mental health, healthy masculinities, and social connection and kept the small cohort, private social media group delivery but developed two models: 'online only' (the original online delivery format) and 'hybrid in-person/online' (combining online delivery with weekly in-person group sessions). Adaptations made included using an overarching Aboriginal and Torres Strait Islander social and emotional wellbeing framework and socio-cultural strengths-based approach; inclusion of modules on health and wellbeing, positive Indigenous masculinities, and respectful relationships; use of Indigenous designs and colours; and prominent placement of images of Aboriginal and Torres Strait Islander male sportspeople, musicians, activists, and local role models. Conclusions: This process resulted in a culturally responsive mental health, masculinities, and social support health promotion program for young Aboriginal and Torres Strait Islander males. Next steps will involve pilot testing to investigate the adapted program's acceptability and feasibility and inform further refinement. Keywords: Aboriginal, Torres Strait Islander, Indigenous, Australia, male, cultural adaptation, social media, mental health, masculinities, social support. [ABSTRACT FROM AUTHOR]

This paper explores the idea of thinking skills approaches as tools for pedagogical inquiry and in so doing seeks to develop the link between the promotion of inquiry-based learning, which is a central tenet of thinking skills, and inquiry-based teaching as an approach to professional development and school improvement. The first part of the paper examines the impact of teaching thinking skills on teachers by drawing upon a systematic review of research evidence. The second part of the paper sets the characteristics identified in the context of research into teachers' development and considers the contribution of a pedagogy based on thinking skills approaches to continuing professional development.

The article discusses the process of judging at the 2007 Interdisciplinary Contest in Modeling (ICM) of the Consortium for Mathematics and Its Applications (COMAP). The competing teams created mathematical models to tackle real world issues on the U.S. kidney transplant system. The judges assessed the papers on three important areas including the effective use of current information regarding the U.S. organ transplant network, appropriateness of model use, and integration of various disciplines including mathematics, science, and ethical principles. The article also discusses the criteria for the outstanding papers and the problems regarding plagiarism.

The article focuses on the views given by judges regarding the outstanding airport screening papers, which were presented by various student teams for the 2003 Interdisciplinary Contest in Modeling. On March 8, 2003 at the U.S. Military Academy, the final judging for the contest took place. The judges evaluated the student team's application for the existing mathematical techniques for the defined problem. The problem dealt with flight scheduling and baggage screening at commercial airport. A definite data was given on the nature of peak hour passengers and flights which was used by them. In addition to the analysis and modeling, the teams were asked to inquire into the emerging technologies in the area of screening of baggage. In addition to the documentation of the theft analysis and modeling efforts, student teams inducted a position paper and two notice that communicated their findings, recommendations and conclusions.

Background: Early identification and treatment of chronic disease is associated with better clinical outcomes, lower costs, and reduced hospitalisation. Primary care is ideally placed to identify patients at risk of, or in the early stages of, chronic disease and to implement prevention and early intervention measures. This paper evaluates the implementation of a technological intervention called Future Health Today that integrates with general practice EMRs to (1) identify patients at-risk of, or with undiagnosed or untreated, chronic kidney disease (CKD), and (2) provide guideline concordant recommendations for patient care. The evaluation aimed to identify the barriers and facilitators to successful implementation. Methods: Future Health Today was implemented in 12 general practices in Victoria, Australia. Fifty-two interviews with 30 practice staff were undertaken between July 2020 and April 2021. Practice characteristics were collected directly from practices via survey. Data were analysed using inductive and deductive qualitative analysis strategies, using Clinical Performance - Feedback Intervention Theory (CP-FIT) for theoretical guidance. Results: Future Health Today was acceptable, user friendly and useful to general practice staff, and supported clinical performance improvement in the identification and management of chronic kidney disease. CP-FIT variables supporting use of FHT included the simplicity of design and delivery of actionable feedback via FHT, good fit within existing workflow, strong engagement with practices and positive attitudes toward FHT. Context variables provided the main barriers to use and were largely situated in the external context of practices (including pressures arising from the COVID-19 pandemic) and technical glitches impacting installation and early use. Participants primarily utilised the point of care prompt rather than the patient management dashboard due to its continued presence, and immediacy and relevance of the recommendations on the prompt, suggesting mechanisms of compatibility, complexity, actionability and credibility influenced use. Most practices continued using FHT after the evaluation phase was complete. Conclusions: This study demonstrates that FHT is a useful and acceptable software platform that provides direct support to general practice in identifying and managing patients with CKD. Further research is underway to explore the effectiveness of FHT, and to expand the conditions on the platform. [ABSTRACT FROM AUTHOR]

Aims: To elicit experiences of patients, family caregivers, and healthcare professionals in intermediate care units (IMCUs) in an academic medical centre in Baltimore, MD related to the challenges and intricacies of multimorbidity management to inform development of a multimorbidity symptom management toolkit. Design: Experience‐based co‐design. Methods: Between July and October 2021, patients aged 55 years and older with multimorbidity admitted to IMCUs at an academic medical centre in Baltimore, Maryland, USA were recruited and interviewed in person. Interdisciplinary healthcare professionals working in the IMCU were interviewed virtually. Participants were asked questions about their role in recognizing and treating symptoms, factors affecting the quality of life, symptom burden and trajectory over time, and strategies that have and have not worked for managing symptoms. An inductive thematic analysis approach was used for analysis. Results: Twenty‐three interviews were conducted: 9 patients, 2 family caregivers, and 12 healthcare professionals. Patients' mean age was 67.5 (±6.5) years, over half (n = 5) were Black or Hispanic, and the average number of comorbidities was 3.67. Five major themes that affect symptom management emerged: (1) the patient–provider relationship; (2) open and honest communication; (3) accessibility of resources during hospitalization and at discharge; (4) caregiver support, training, and education; and (5) care coordination and follow‐up care. Conclusion: Patients, caregivers, and healthcare professionals often have similar goals but different priorities for multimorbidity management. It is imperative to identify shared priorities and target holistic interventions that consider patient and caregiver experiences to improve outcomes. Implications for the Profession and/or Patient Care and Impact: This paper addresses the paucity of research related to the shared experience of disease trajectory and symptom management for people living with multimorbidity. We found that patients, caregivers, and healthcare professionals often have similar goals but different care and communication priorities. Understanding differing priorities will help better design interventions to support symptom management so people with multimorbidity can have the best possible quality of life. Reporting Method: We have adhered to the Consolidated Criteria for Reporting Qualitative Studies (COREQ) guidelines in our reporting. Patient or Public Contribution: This study has been designed and implemented with patient and public involvement throughout the process, including community advisory board engagement in the project proposal phase and interview guide development, and member checking in the data collection and analysis phases. The method we chose, experience‐based co‐design, emphasizes the importance of engaging members of a community to act as experts in their own life challenges. In the coming phases of the study, the public will be involved in developing and testing a new intervention, informed by these qualitative interviews and co‐design events, to support symptom management for people with multimorbidity. [ABSTRACT FROM AUTHOR]

In this article the author comments on the aspects of the sweet spot papers for the Mathematical Contest in Modeling (MCM) in the U.S. He is critical in making the MCM a learning experience for the participating teams and an enriching one for the judges in the new technology. Also investigated are the keywords in the problem statement which should be given a mathematical meaning in the context of a model.

The article reviews the article "Moody's Mega Math Challenge Winning Paper: Making Sense of the 2010 Census," by A. Das Sarma and colleagues, which appeared in the "SIAM Undergraduate Research Online" (SIURO) 3 on August 4, 2010.

The article explains the inspiration behind the Outstanding River Problem for the 2012 Mathematical Contest in Modeling. According to the author, the problem was inspired by a research project for the Grand Canyon National Park in Arizona. She reveals that her team developed a mathematical model to simulate white water rafting traffic along the Colorado River. She adds that their model was used as a tool by National Park Service river managers for exploring scheduling rafting traffic options.

Statistical body shape models (SBSM) provide compact, flexible representations of body shape that can be implemented in design software. However, few SBSMs have been created to represent adults in supported seated postures that are relevant for the design of seated environments, and none has incorporated the effects of age. This paper presents an SBSM based on surface laser-scan data from 155 U.S. adults. The data were processed to obtain homologous mesh structure and symmetric geometry, and the processed data were statistically analysed using principal component analysis to obtain a compact representation of the data variance. Regression analysis was conducted to predict body size and shape from stature, body mass index, ratio of sitting height to stature, sex, and age. The resulting model allows rapid generation of realistic body models for applications, including product design, accommodation assessment, and safety system optimisation. The model is publicly accessible at HumanShape.org. Practitioner summary: This paper presents a statistical model that represents adult body shapes in a supported seated posture based on 3 D anthropometric measurements. This model is the first whole-body parametric model known to incorporate age effects based on data extending beyond 65 years of age. [ABSTRACT FROM AUTHOR]

Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about whether the salient words agree with those identified by humans as important. Objectives: The current study examines in‐line annotations from human annotators and saliency map annotations from a deep learning model (ELECTRA transformer) to understand how well both humans and machines provide evidence for their assigned label. Methods: Data were responses to test items across a mix of United States subjects, states, and grades. Humans were trained to annotate responses to justify a crisis alert label and two model interpretability methods (LIME, Integrated Gradients) were used to obtain engine annotations. Human inter‐annotator agreement and engine agreement with the human annotators were computed and compared. Results and Conclusions: Human annotators agreed with one another at similar rates to those observed in the literature on similar tasks. The annotations derived using the integrated gradients (IG) agreed with human annotators at higher rates than LIME on most metrics; however, both methods underperformed relative to the human annotators. Implications: Saliency map‐based engine annotations show promise as a form of explanation, but do not reach human annotation agreement levels. Future work should examine the appropriate unit for annotation (e.g., word, sentence), other gradient based methods, and approaches for mapping the continuous saliency values to Boolean annotations. Lay Description: What is known about subject matter: Explainability of deep learning models is complicated by their large size, design, and complexity.Explainability methods in deep learning exist but have not been rigorously tested.Saliency maps, as a form of explainability, can be used to generate engine annotations.Human annotations can serve as basis for comparison. What paper adds to knowledge: How well human annotations work in one domain (crisis paper identification).How well two engine annotation methods match human annotations. Implications of study findings: Human annotations have reasonable reliability.Engine annotations have lower reliability than humans.Model‐based engine annotations perform better than model‐agnostic engine annotations. [ABSTRACT FROM AUTHOR]

Background: Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. Lay Description: What is already known about this topic: Open‐ended questions are used by teachers in the domain of mathematics to assess their students understanding but automated support for these types of questions are limited in online learning platforms. Recent advancements in areas of machine learning and natural language processing have led to promising results in a range of domains and applications. What this paper adds: Emulating how teachers identify similar student answers can be used to build tools that support teachers in assessing and providing feedback to student open‐ended work. Implications for practice: Developing better automated supports can increase the amount of direct feedback students receive to guide their learning. [ABSTRACT FROM AUTHOR]

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In this article the author comments on the modeling contest for the Outstanding Geographic Profiling Papers on the investigation of serial criminals in the U.S. She is critical on the assumption which concerns the probability distributions, anchor points and mathematical procedures. Also investigated is the summary which is important, particularly in early judging.

This paper provides a microanalysis of one Algebra I teacher's instruction to explore the advantages that are afforded us by coordinating two perspectives to document and account for the teacher's mathematical understandings. We use constructs associated with Stein, Grover and Henningsen's domain of mathematical didactics and Realistic Mathematics Education's instructional design theory to infer what the teacher might understand to effectively implement her instructional goals and, more importantly, support student learning. By coordinating these perspectives, we developed a working framework for analyzing the teacher's classroom practice retrospectively. For example, we illustrate how the mathematical possibilities related to one student's question might inform the teacher's decisions as she initiates shifts in students' self-generated models. Additionally, we illustrate how the teacher's decision to capitalize on particular students' models contributes in part to the kinds of mathematical ideas that can be explored and the connections students can make among those ideas. More generally, we explore the utility of coordinating these two perspectives to understand the landscape of ideas that teachers might traverse to align their practices with reform recommendations in the United States.

The article offers the author's comments regarding the aspects of the Interdisciplinary Contest in Modeling (ICM) in the U.S. It notes that the ICM is an opportunity for student teams to tackle real-world problems which require a breadth of understanding in multiple academic subjects. It also notes that eight judges were gathered to select the successful entries out of the submitted research for the competition.

The article discusses the mathematical models used to analyze and simulate the U.S. kidney transplant system. It focuses on algorithms for matching donors to kidneys. It mentions that kidneys became available when a person dies and must be transplanted quickly. A proper scheme should be developed to find a compatible recipient for the kidney. The article also discusses using a bipartite graph to pair off living donors and recipients. It describes the problem of matching kidney donors and recipients as dynamic. It also analyzes the feasibility and effectiveness of the kidney paired donation model.

Focuses on a mathematical model for exhaustion of oil. Way to estimate cumulative usage of a nonrenewable resource whose rate of use is increasing at a fixed rate; Information that the U.S. has recoverable reserves of coal that would last about 250 years; Affect of the decline of the dollar on the oil prices in U.S.

This article presents the comments by the author on the 2001 Interdisciplinary Contest in Modeling about research concerning water pollution caused by zebra mussel in the United States. They cause an economic damage of $5 billion annually. According to her, there is a need of developing mathematical models that can predict and counter the danger posed by zebra mussel by using the existing data sets for water bodies. She appreciates the worldwide attention given to this problem and the work done by interdisciplinary student teams.

This article presents the views of the judges of the 3rd Interdisciplinary Contest in Modeling that was held in the United States. The judges appreciate the effort and creativity of the teams that participated in the contest. They said that the interdisciplinary problem of preventing the Zebra mussel growth was very difficult and it required a combination of science and mathematics to solve it. Zebra mussels are causing tremendous problems for the ecosystem and the regional economies throughout the eastern waterways of the United States.

In this paper we present three cases of instructional design that illustrates both horizontal didactising, the activity of using already established principles to design instruction, and vertical didactising the activity of developing new tools and principles for instructional design. The first case illustrates horizontal didactising by elaborating how the constructs chains of signification and models were used to design an instructional sequence involving linear growth. The second and third cases illustrate vertical didactising by developing argumentation analyses and generative listening, respectively, as instructional design tools. In the second case, argumentation analyses emerge as a tool that other designers can use to anticipate the quality of conversations that can occur as students engage in tasks prior to implementing the instructional sequence. The third case develops the notion of generative listening as a conceptual tool within the context of designing differential equations instruction to gain insights into what are, for students, experientially-real starting points that are mathematical in nature and to provide inspirations for revisions to instructional sequences.

Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated. In this paper, we establish useful estimands for the evaluation of large-scale policies. We discuss that imputation of missing potential outcomes relies on an assumption which, even though untestable, can be partially evaluated using observed data. We illustrate an approach to evaluate this key causal assumption and facilitate model elicitation based on data from the time interval before policy initiation and using classic statistical techniques. As an illustration, we study the Hospital Readmissions Reduction Program (HRRP), a US federal intervention aiming to improve health outcomes for patients with pneumonia, acute myocardial infraction, or congestive failure admitted to a hospital. We evaluate the effect of the HRRP on population mortality among the elderly across the US and in four geographic subregions, and at different time windows. We find that the HRRP increased mortality from pneumonia and acute myocardial infraction across at least one geographical region and time horizon, and is likely to have had a detrimental effect on public health. [ABSTRACT FROM AUTHOR]

Health care providers have highlighted the need for tools and resources that support promotion of exercise behavior within comprehensive multiple sclerosis (MS) care. This study involved a final quality improvement evaluation of exercise promotion models and materials for inclusion within this setting. Our research team distributed a paper-based survey containing Likert scales, open answer questions, and copies of the models for editing. We distributed this survey among health care providers across the United States. We conducted a novel mixed-methods analysis evaluating quantitative, qualitative, and creative data. We received completed surveys from 13 health care providers who strongly rated the clarity and applicability of the models and materials, and reported that no major improvements were necessary. The minor improvements were specific per comprehensive MS care center. The feedback indicated that the "Exercise in Medicine" models and materials are guides such that the processes should be integrated into real world practice by amending roles and responsibilities with the team members and structure per comprehensive MS care center. This paper presents finalized models and materials for exercise promotion within comprehensive MS care that are ready to be tested for feasibility and efficacy in a clinical trial. Health care providers require support to promote exercise within the context of comprehensive MS care. The practice models in this article provide guides regarding how to promote exercise in this context. Implementing these exercise promotion guides can reduce the burden of neurologists, and ensure patients receive exercise support from appropriate providers. These guides should be implemented within the context of each individual care center, and not as an explicit step by step guide as each care center is unique. [ABSTRACT FROM AUTHOR]

BACKGROUND: Currently, there is a need for models, methods, and tools that allow ergonomics/human factor (E/HF) practitioners to assess the level of E/HF integration into organizations from a macroergonomics perspective. OBJECTIVE: This paper aims to propose the Ergonomic Maturity Model (EMM) and the tools for its application as a framework for integrating E/HF in organizations. METHODS: The EMM is a macroergonomic tool that allows stakeholders to evaluate the degree of development and integration of E/HF in the organization based on a participatory and macroergonomic approach. The EMM classifies organizations into five gradual levels of maturity: Ignorance, Understanding, Experimentation, Regular use, and Innovation. RESULTS: In this paper, we provide a three-stage procedure for guiding the application of the EMM: preparation of the evaluation, evaluation, and improvement plan and implementation. We include four tools developed specifically for applying EMM in organizations: evaluation matrix, weighting questionnaire, quick questionnaire, and prioritization matrix. Also, we present a Colombian floriculture company's case study to exemplify the use of the EMM. CONCLUSIONS: The EMM provides a framework for integrating E/HF into organizations from the macroergonomics approach. E/HF practitioners can find in the EMM a tool to help them channel the actions taken by the different organizational actors to improve the safety, health, well-being, and performance of work systems. Finally, it should be noted that further studies on the reliability and validity of the EMM are needed, which would contribute to demonstrating that the EMM can effectively and successfully guide change in E/HF maturity levels in organizations. [ABSTRACT FROM AUTHOR]

The Supreme Court in 1911, on the occasion of the first major test of the Sherman Act, ordered the dissolution of the Standard Oil Trust. In his 1958 paper John McGee argued that predatory pricing is, in general, irrational and, relying solely on the information in the Trial Record related to that decision, concluded that Standard Oil did not engage in predatory pricing. His paper has had an extraordinary influence on both antitrust policy in the United States and economic lore. This paper documents the breadth and scope of the influence of McGee's paper and offers several possible explanations for it. We suggest four reasons: (1) the lack of a theoretical challenge for 25 years, (2) the failure of scholars to replicate McGee's empirical findings, (3) the unique status of the Standard Oil case in the history of American antitrust and (4) the influence of the Chicago School on economic and legal thinking. [ABSTRACT FROM AUTHOR]

With the growing prevalence of chronic conditions driving 85% of all healthcare costs, digital health offers a promising opportunity to reverse disease and improve health at-scale. The healthcare industry's predominant approach to behavior change is performance-based with a focus on goals and tracking. This has not reversed the epidemic of chronic diseases and also can harm chronically ill and vulnerable patients via perceived failure-induced loss of motivation. Still nascent, the digital health industry is uniquely positioned to adopt and scale new and better behavior change approaches. In this paper, we present the theoretical foundation and initial findings of a neuroscience-based behavior change approach—what we call the Iterative Mindset MethodTM. We discuss its promise, as a potentially more effective, neuroscience-based approach to changing health behaviors long-term, particularly in vulnerable populations. We conclude with avenues for future research. [ABSTRACT FROM AUTHOR]

This paper provides an alternative approach to penalized regression for model selection in the context of high‐dimensional linear regressions where the number of covariates is large, often much larger than the number of available observations. We consider the statistical significance of individual covariates one at a time, while taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time Multiple Testing (OCMT) procedure, and use ideas from the multiple testing literature to control the probability of selecting the approximating model, the false positive rate, and the false discovery rate. OCMT is easy to interpret, relates to classical statistical analysis, is valid under general assumptions, is faster to compute, and performs well in small samples. The usefulness of OCMT is also illustrated by an empirical application to forecasting U.S. output growth and inflation. [ABSTRACT FROM AUTHOR]

Purpose/Objective: Individuals with historically oppressed identities, such as disabled or racialized minorities, face inequities across all societal institutions, including education, criminal justice, and healthcare. Systems of oppression (e.g., ableism, racism) lead to inequities that have ultimately contributed to disproportionate rates of COVID-19 morbidity and mortality in the United States. In the midst of the COVID-19 pandemic, increased public attention regarding police brutality toward Black people and the reinvigoration of the national Black Lives Matter (BLM) movement further highlighted the detrimental effects of oppressive systems and the urgent need to promote equity in the United States. The disproportionate number of COVID-19-related deaths and police brutality are inextricably connected, as both are products of oppression toward minoritized communities. The co-occurrence of the pandemic and BLM movement protests also creates an opportunity for critical discourse on the intersection of ableism and anti-Black racism specifically within the field of rehabilitation psychology. Research Method/Design: The overarching goals of this review are to apply the Intersectional Ecological Model with the addition of the chronosystem to illustrate how systems of oppression lead to health disparity in COVID-19 survivorship and to provide recommendations to promote health equity. Conclusions/Implication: As the COVID-19 pandemic shifts to an endemic and efforts to eliminate oppressive systems continue, rehabilitation psychologists have an ongoing, evolving, and shared responsibility to employ socially-responsive solutions to promote optimal functioning for patients, families, and communities. Impact and Implications: Disabled and Black communities continue to face inequities across all societal systems. The COVID-19 pandemic and simultaneous Black Lives Matter Movement protests brought systemic inequities faced by disabled and Black communities to the forefront of the nation's attention, creating an opportunity to explore the intersection of ableism and anti-Black racism on COVID-19 survivorship. This paper uses the Intersectional Ecological Model to illustrate how systemic oppression contributes to inequity in COVID-19 survivorship for Black disabled communities. Rehabilitation psychologists can leverage the information in this paper to foster socially conscious clinical practice, research, training, and advocacy. [ABSTRACT FROM AUTHOR]

This paper considers the relationship between income and wealth using data from three sources: the Survey of Consumer Finances (SCF), the IRS and Forbes Magazine. Because of the legal and ethical constraints on the use of some of these data, the focus of the paper is limited to a few issues that are relevant to improving the design of the SCF and understanding better the quality of the information collected. The most important goal of the paper is estimation of functional relationships between income and wealth. Work to this end results in model predictions that are more effective than using income alone as a proxy for wealth. However, it is clear that the relationship is not strong, and more work is needed, particularly to address the noise introduced into the relationship by year-to-year income variability. [ABSTRACT FROM AUTHOR]

A documentary review was carried out on producing and publishing research papers on studying differential equations, mathematical models, and education. The bibliometric analysis proposed in this paper was to know the main characteristics of the volume of publications registered in the Scopus database during 2017-2021, identifying 90 publications. The information provided by the said platform was organized employing tables and figures categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics were described, a qualitative analysis was used to refer to the position of different authors on the proposed topic. Among the main findings of this research, it is found that the United States, with 13 publications, was the country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The area of knowledge that made the greatest contribution to the construction of bibliographic material referring to the study of differential equations, mathematical models, and education was Mathematics, with 37 published documents, and the type of publication that was most used during the period mentioned above was the conference article, which represents 47% of the total scientific production. [ABSTRACT FROM AUTHOR]

The visual presentation of a set of data facilitates its understanding and explanation. This paper describes how hidden surface routines can be used to construct two-dimensional portrayals of three-dimensional data. Data from a study on consumer preferences is used to illustrate the process. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to make a detailed comparison of three macroeconometric models of the U.S. economy with regard to their predictive ability and policy multiplier properties. Three models are the St. Louis Model, the Wharton Model and the M.P.S. Model. These models are constructed with different emphasis on the treatment of the monetary phenomena, depending upon the philosophy of the model builder. They may respectively be called a monetarist model, a Keynesian-type income-expenditure model, and a structural model which emphasizes the monetary structure for the stabilization policy analysis. The structure of each model is not discussed in this paper but only a brief sketch of their special features is given in the next section. The predictive performance and the policy multiplier analysis are discussed in the 3rd and 4th sections, followed by some concluding remarks. [ABSTRACT FROM AUTHOR]

Background: There is an increasing number of technological resources available to speech and language therapists (SLTs) for use in clinical practice, but the factors that influence SLTs' selection and use of such resources are not well understood. In related fields, technology acceptance models have been employed to explain users' adoption of technology and to inform the advancement of empirically supported technological resources. Aims: To determine the factors that influence SLTs' use of technology for clinical practice by testing a model of their technology acceptance and use. Methods & Procedures: We surveyed 209 practising SLTs in the United States representative of the speech and language membership of the American Speech–Language–Hearing Association (ASHA). Participants completed a 38‐item electronic survey representing four categories: (1) technology use, (2) technology attitudes and factors influencing technology use, (3) employment information and (4) demographics. Items measuring technology attitudes served as indicators of the research model, which mapped the primary relationships of a technology acceptance model. Survey data were collected before the Covid‐19 pandemic. Outcomes & Results: The research model accounted for 66% of the variance in SLTs' behavioural intention to use technology, which significantly and positively predicted the amount of time they reportedly spent using technology in the workplace. Subjective norms and attitudes towards technology use directly predicted the intention to use technology. Perceived usefulness and ease of use indirectly predicted intention to use technology. Survey respondents reported using technology during 48% (SD = 24%) of their overall weekly work hours on average, with a large majority reporting using technology at least once per week for planning (89% of respondents), assessment (66% of respondents) or intervention (90% of respondents). Conclusions & Implications: These findings statistically explain the relationships between SLTs' attitudes and their intention to use technology for clinical practice, contributing to our understanding of why SLTs adopt certain technologies. We also detail the nature and frequency of technology use in the clinical practice of SLTs. Future directions for this work include further exploring use categories, employing direct measurements of technology use and exploring the impact of recent changes in SLT service delivery due to the Covid‐19 pandemic on SLTs' technology attitudes. What this paper addsWhat is already known on the subjectExisting research about the adoption and use of technological resources by SLTs indicates that they select tools based on convenience, cost and recommendations by others.What this paper adds to existing knowledgeThis study is the first to develop and test a research model of SLTs' technology attitudes. The findings from model testing demonstrate the significant predictors of SLTs' behavioural intention to use technology for clinical purposes. Intent of use is related to how much SLTs use technology in the workplace.What are the potential or actual clinical implications of this work?The present findings can inform interventions targeting the design and adoption of electronic SLT resources that are empirically supported. [ABSTRACT FROM AUTHOR]