Measurement-based care (MBC) can be defined as the practice of basing clinical care on client data collected throughout treatment. MBC is considered a core component of numerous evidence-based ...practices (e.g., Beck & Beck, 2011; Klerman, Weissman, Rounsaville, & Chevron, 1984) and has emerging empirical support as an evidence-based framework that can be added to any treatment (Lambert et al., 2003, Trivedi et al., 2007). The observed benefits of MBC are numerous. MBC provides insight into treatment progress, highlights ongoing treatment targets, reduces symptom deterioration, and improves client outcomes (Lambert et al., 2005). Moreover, as a framework to guide treatment, MBC has transtheoretical and transdiagnostic relevance with broad reach across clinical settings. Although MBC has primarily focused on assessing symptoms (e.g., depression, anxiety), MBC can also be used to assess valuable information about (a) symptoms, (b) functioning and satisfaction with life, (c) putative mechanisms of change (e.g., readiness to change), and (d) the treatment process (e.g., session feedback, working alliance). This paper provides an overview of the benefits and challenges of MBC implementation when conceptualized as a transtheoretical and transdiagnostic framework for evaluating client therapy progress and outcomes across these four domains. The empirical support for MBC use is briefly reviewed, an adult case example is presented to serve as a guide for successful implementation of MBC in clinical practice, and future directions to maximize MBC utility are discussed.
•Highlight the definition of measurement-based care (MBC) as it has been applied in the literature•Review clinical utility of MBC as a transtheoretical and transdiagnostic framework for enhancing mental health treatment•Potential challenges associated with implementation of MBC in community settings are discussed•Examples of MBC assessment domains and measure resources for clinician implementation of MBC provided•Case example employing MBC protocol to guide treatment for complex clients
Research in Quality Control (QC) process digitalisation has principally focused on novel technologies for data acquisition and processing during construction. In contrast, this manuscript focuses on ...the planning phase and proposes a method that analyses the as-planned 4D Building Information Model (‘BIM model’) to obtain: (1) an exhaustive list of all geometric QC instances to be checked during construction; (2) and an initial schedule of when these checks can be conducted. A rule-based approach is employed to identify the geometric QC instances in the BIM model represented as a graph. The method is demonstrated with three real case studies, including building and rail infrastructure projects, with geometric specifications encoded from the EN 13670 and EN 1090–2 standards as well as other relevant industry sources. The list of QC instances outputted by the method can be used as-is by QC surveyors and managers, or can serve as input to automated QC technologies.
•Method to automatically generate an exhaustive list of geometric QC instances from a BIM model•Geometric specifications are digitised into a dictionary of geometric QC rules•Each instance of the geometric QC rules is detected using a graph matching approach•Method can also produce an initial QC schedule, if 4D BIM model is provided•Validation is performed using a realistic building and two real rail infrastructure projects
Household labor is commonly defined as a set of physical tasks such as cooking, cleaning, and shopping. Sociologists sometimes reference non-physical activities related to “household management,” but ...these are typically mentioned in passing, imprecisely defined, or treated as equivalent to physical tasks. Using 70 in-depth interviews with members of 35 couples, this study argues that such tasks are better understood as examples of a unique dimension of housework: cognitive labor. The data demonstrate that cognitive labor entails anticipating needs, identifying options for filling them, making decisions, and monitoring progress. Because such work is taxing but often invisible to both cognitive laborers and their partners, it is a frequent source of conflict for couples. Cognitive labor is also a gendered phenomenon: women in this study do more cognitive labor overall and more of the anticipation and monitoring work in particular. However, male and female participation in decision-making, arguably the cognitive labor component most closely linked to power and influence, is roughly equal. These findings identify and define an overlooked—yet potentially consequential—source of gender inequality at the household level and suggest a new direction for research on the division of household labor.
While unavoidable, inspections, progress monitoring, and comparing as-planned with as-built conditions in construction projects do not readily add tangible intrinsic value to the end-users. In ...large-scale construction projects, the process of monitoring the implementation of every single part of buildings and reflecting them on the BIM models can become highly labour intensive and error-prone, due to the vast amount of data produced in the form of schedules, reports and photo logs. In order to address the mentioned methodological and technical gap, this paper presents a framework and a proof of concept prototype for on-demand automated simulation of construction projects, integrating some cutting edge IT solutions, namely image processing, machine learning, BIM and Virtual Reality. This study utilised the Unity game engine to integrate data from the original BIM models and the as-built images, which were processed via various computer vision techniques. These methods include object recognition and semantic segmentation for identifying different structural elements through supervised training in order to superimpose the real world images on the as-planned model. The proposed framework leads to an automated update of the 3D virtual environment with states of the construction site. This framework empowers project managers and stockholders with an advanced decision-making tool, highlighting the inconsistencies in an effective manner. This paper contributes to body knowledge by providing a technical exemplar for the integration of ML and image processing approaches with immersive and interactive BIM interfaces, the algorithms and program codes of which can help replicability of these approaches by other scholars.
•This study proposed a framework for automated real-time simulation of the construction projects.•Feasibility of integrating real-world images with BIM and virtual world models is proven.•The algorithm is developed for splicing site and BIM data through image semantic segmentation and BIM virtual photogrammetry.•Exploration of BIM-led deep learning provides a method for reinforcement learning through spatial inference.•The paper contributes to the BIM and Deep learning communications and supports emerging convergent reality literature.
Control theory and other frameworks for understanding self-regulation suggest that monitoring goal progress is a crucial process that intervenes between setting and attaining a goal, and helps to ...ensure that goals are translated into action. However, the impact of progress monitoring interventions on rates of behavioral performance and goal attainment has yet to be quantified. A systematic literature search identified 138 studies (N = 19,951) that randomly allocated participants to an intervention designed to promote monitoring of goal progress versus a control condition. All studies reported the effects of the treatment on (a) the frequency of progress monitoring and (b) subsequent goal attainment. A random effects model revealed that, on average, interventions were successful at increasing the frequency of monitoring goal progress (d+ = 1.98, 95% CI 1.71, 2.24) and promoted goal attainment (d+ = 0.40, 95% CI 0.32, 0.48). Furthermore, changes in the frequency of progress monitoring mediated the effect of the interventions on goal attainment. Moderation tests revealed that progress monitoring had larger effects on goal attainment when the outcomes were reported or made public, and when the information was physically recorded. Taken together, the findings suggest that monitoring goal progress is an effective self-regulation strategy, and that interventions that increase the frequency of progress monitoring are likely to promote behavior change.
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The provision of progress monitoring is mandated under federal law for children receiving special education services. In recent years, professional organizations have recommended that this provision ...be extended to include children receiving services within multitiered systems of support. Organizations representing the field of early childhood education have embraced this focus on progress monitoring to include all children, regardless of ability or disability. To understand how to effectively prepare preschool educators to engage in progress monitoring practices, we conducted a systematic review of the literature. Results of repeated and extended search methods identified only four studies. We present our findings and stress the need for researchers and funding agencies to work toward establishing a rigorous body of literature devoted to professional development and teacher training surrounding progress monitoring.
Facial recognition technology is now being introduced across various aspects of public life. This includes the burgeoning integration of facial recognition and facial detection into compulsory ...schooling to address issues such as campus security, automated registration and student emotion detection. So far, these technologies have largely been seen as routine additions to school systems with already extensive cultures of monitoring and surveillance. While critical commentators are beginning to question the pedagogical limitations of facially driven learning, other this article contends that school-based facial recognition presents a number of other social challenges and concerns that merit specific attention. This includes the likelihood of facial recognition technology altering the nature of schools and schooling along divisive, authoritarian and oppressive lines. Against this background, the article considers whether or not a valid case can ever be made for allowing this form of technology in schools.
The ever increasing volume of visual data due to recent advances in smart devices and camera-equipped platforms provides an unprecedented opportunity to visually capture actual status of construction ...sites at a fraction of cost compared to other alternatives methods. Most efforts on documenting as-built status, however, stay at collecting visual data and updating BIM. Hundreds of images and videos are captured but most of them soon become useless without properly being localized with plan document and time. To take full advantage of visual data for construction performance analytics, three aspects (reliability, relevance, and speed) of capturing, analyzing, and reporting visual data are critical. This paper 1) investigates current strategies for leveraging emerging big visual data and BIM in construction performance monitoring from these three aspects, 2) characterizes gaps in knowledge via case studies and structures a road map for research in visual sensing and analytics.
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•Investigates current strategies for leveraging emerging big visual data and BIM•Discusses challenges of implementing BIM-driven visual analytics in construction•Presents various methods for leveraging different types of visual data with BIM•Presents multiple case studies to validate practical significance and opportunities
Image-based analysis techniques offer a robust way to solve engineering problems due to the availability of visual data (e.g., surveillance cameras). Hence, research efforts have focused on applying ...Image-based techniques in the construction industry to improve the safety and productivity of construction operations as well as the resilience and sustainability of the construction assets. This paper explores the state-of-the-art in Image-based analysis techniques and their applications in construction. Over 100 journal papers were retrieved from the Scopus database for an in-depth review of major applications, benefits, and areas of future research potential. Accordingly, Three main research directions were identified that utilize image-based technologies: (1) construction safety; (2) progress monitoring; and (3) damage assessment. It is observed that most research efforts focused on object detection (e.g., hardhats, defects) for safety inspection and repair planning. Potential future developments include integrating object detection with quantification and sizing techniques to develop more comprehensive applications.
•Reviewed 100 recent articles (2015–2020) on the use of image-based analysis technology in construction.•The researched papers show three main research trends: Construction Safety, Progress Monitoring, and Damage Assessment.•Artificial Neural Networks (ANNs) is the most commonly used technique for image-based analysis models, especially after 2017.•Technical challenges and research gaps are identified.•Developing applications that provide high-level understanding of the overall scene is an area of future research potential.
Computer adaptive tests have become popular assessments to screen students for academic risk. Research is emerging regarding their use as progress monitoring tools to measure response to instruction. ...We evaluated the accuracy of the trend-line decision rule when applied to outcomes from a frequently used reading computer adaptive test (i.e., Star Reading SR) and frequently used math computer adaptive test (i.e., Star Math SM). Analyses of extant SR and SM data were conducted to inform conditions for simulations to determine the number of assessments required to yield sufficient sensitivity (i.e., probability of recommending an instructional change when a change was warranted) and specificity (i.e., probability of recommending maintaining an intervention when a change was not warranted) when comparing performance to goal lines based upon a future target score (i.e., benchmark) as well as normative comparisons (50th and 75th percentiles). The extant dataset of SR outcomes consisted of monthly progress monitoring data from 993 Grade 3, 804 Grade 4, and 709 Grade 5 students from multiple states in the United States northwest. Data for SM were also drawn from the northwest and contained outcomes from 518 Grade 3, 474 Grade 4, and 391 Grade 5 students. Grade level samples were predominately White (range = 59.89%–67.72%) followed by Latinx (range = 9.65%–15.94%). Results of simulations suggest that when data were collected once a month, seven, eight, and nine observations were required to support low-stakes decisions with SR for Grades 3, 4, and 5, respectively. For SM, nine, ten, and eight observations were required for Grades, 3, 4, and 5, respectively. Given the length of time required to support reasonably accurate decisions, recommendations to consider other types of assessments and decision-making frameworks for academic progress monitoring are provided.
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