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.

Schools are a critical setting to promote healthy youth development through the provision of evidence-based programs (EBPs), yet preventive EBPs in schools are underutilized. The Exploration, Preparation, Implementation, Sustainment (EPIS) framework highlights numerous factors that may influence program adoption during the Exploration phase and progress monitoring during the Implementation phase. However, no research has systematically and simultaneously identified the factors that influence school administrators\' decision-making during these important processes. We conducted semi-structured interviews with 24 school administrators in the Midwestern region of the U.S. to understand how they weigh various considerations that inform their adoption and progress monitoring of prevention programs. Results indicated that school administrators consider five separate factors during the adoption decision, prioritized in the following order: need for the program, school community buy-in, contextual fit, resources, and program characteristics (including the evidence-base). Further, administrators consider five indicators to monitor program performance, prioritized as follows: intervention fidelity, quantitative and qualitative data that determine if the identified need was met, school community buy-in, resource consumption, and program characteristics. Implications for prevention scientists and suggestions for future research are discussed.

UNASSIGNED: Globally, outpatient programs for substance use disorder (SUD) treatment have gained prominence. To assess the broader clinical implications of this trend we investigated shifts in functioning experienced by outpatients undergoing treatment.
UNASSIGNED: We describe the clinical characteristics of a cohort of 93 SUD patients in a Norwegian outpatient treatment clinic. Using paired-samples t-tests, we examined changes in perceived functioning, mental distress, and other clinically relevant outcome variables in a 5-month time interval during the treatment course.
UNASSIGNED: We obtained follow-up data for 67 (72%) of the included patients, with no significant difference in patient-related factors between those who completed the treatment course and those who were not assessed at follow-up. Perceived functioning increased significantly from study inclusion (Time 0) (mean 19.8, standard deviation ± 8.8) to its conclusion (Time 1) (24.3, ±9.3; t (66) = 4.5, (95% CI: 2.5-6.5, p < 0.001). We also identified significant improvement in most other measured variables, including mental distress, self-reported sleep quality, restlessness, and obsessive thinking. Substance use-related variables showed a modest, non-significant improvement at T1.
UNASSIGNED: During a 5-month course of outpatient treatment, patients\' subjective experience of functioning improved significantly. Those with the lowest functioning levels at T0 improved the most. Structured monitoring may be a valuable clinical tool for personalizing intervention, enhancing treatment outcomes, and supporting the clinical decision-making process.

This study investigated coded data retrieved from clinical dashboards, which are decision-support tools that include a graphical display of clinical progress and clinical activities. Data were extracted from clinical dashboards representing 256 youth (M age = 11.9) from 128 practitioners who were trained in the Managing and Adapting Practice (MAP) system (Chorpita & Daleiden in BF Chorpita EL Daleiden 2014 Structuring the collaboration of science and service in pursuit of a shared vision. 43(2):323 338. 2014, Chorpita & Daleiden in BF Chorpita EL Daleiden 2018 Coordinated strategic action: Aspiring to wisdom in mental health service systems. 25(4):e12264. 2018) in 55 agencies across 5 regional mental health systems. Practitioners labeled up to 35 fields (i.e., descriptions of clinical activities), with the options of drawing from a controlled vocabulary or writing in a client-specific activity. Practitioners then noted when certain activities occurred during the episode of care. Fields from the extracted data were coded and reliability was assessed for Field Type, Practice Element Type, Target Area, and Audience (e.g., Caregiver Psychoeducation: Anxiety would be coded as Field Type = Practice Element; Practice Element Type = Psychoeducation; Target Area = Anxiety; Audience = Caregiver). Coders demonstrated moderate to almost perfect interrater reliability. On average, practitioners recorded two activities per session, and clients had 10 unique activities across all their sessions. Results from multilevel models showed that clinical activity characteristics and sessions accounted for the most variance in the occurrence, recurrence, and co-occurrence of clinical activities, with relatively less variance accounted for by practitioners, clients, and regional systems. Findings are consistent with patterns of practice reported in other studies and suggest that clinical dashboards may be a useful source of clinical information. More generally, the use of a controlled vocabulary for clinical activities appears to increase the retrievability and actionability of healthcare information and thus sets the stage for advancing the utility of clinical documentation.

Curriculum-based measurement (CBM) is an approach to measuring student academic growth and evaluating the effectiveness of instruction (Deno, Exceptional Children, 52, 219-232, 1985) that was developed, in part, based on characteristics of applied behavior analysis. Learning to administer and use CBM data is commonly part of teacher preparation programs, but less common in behavior analysis graduate programs (Schreck et al. Behavioral Interventions, 31, 355-376, 2016; Schreck & Mazur, Behavioral Interventions, 23, 201-212, 2008). This article describes a sequence of steps that educational teams can follow to use CBM within the multi-tiered system of support (MTSS) framework. These steps include (1) selecting a CBM publisher and gathering materials; (2) practicing administering and scoring CBM; (3) administering, scoring, and comparing student scores to grade-level benchmarks; (4) using CBM data to write ambitious and realistic IEP goals; and (5) using data-based individualization. Each step is described and includes a description of a case study that is based on our experiences working with pre-service teacher candidates, and special education and behavior analysis graduate students in K-12 and after-school instructional programs.

OBJECTIVE: Clear and effective indicators for early detection of severe coronavirus disease 2019 (COVID-19) are insufficient. We investigated the clinical value of the plasma SARS-CoV-2 N antigen (plasma N antigen) for severe COVID-19 early identification and disease progression monitoring.
METHODS: A cross-sectional study compared the diagnostic value of plasma N antigen levels detected within two days after hospital admission in 957 patients with COVID-19 during the BA2.2 outbreak in Shanghai (April 6-June 15, 2022). A follow-up study analyzed the plasma N antigen prognostic value in 274 non-severe patients, and a longitudinal study evaluated its continuous monitoring value in 16 patients with COVID-19 grade changes.
RESULTS: Plasma N antigen concentrations were significantly higher in severely ill than in non-severely ill patients. The plasma N antigen was superior to nasopharyngeal nucleic acid CT values and established COVID-19 blood biomarkers in identifying severe COVID-19. Patients with high plasma N-antigen concentrations at initial admission were more prone to developing severe COVID-19. The changes in plasma N antigen concentrations were consistent with disease progression. Two logistic regression models, including and excluding plasma N antigen, were established, with model 1 (including plasma N antigen) (AUC = 0.971, 0.958-0.980) yielding a better diagnostic value for severe COVID-19 than Model 2 (plasma N antigen excluded).
CONCLUSIONS: The plasma N antigen is superior to nasopharyngeal nucleic acids and established COVID-19 blood biomarkers for severe COVID-19 early recognition and progression monitoring, enabling the most accurate patient triaging and efficient utilization of medical resources.

Utilisation of intensive inpatient treatment for eating disorders (EDs) has climbed in the last decade, illuminating a need for better consensus on what constitutes effective treatment and context-appropriate progress/outcome monitoring during residential stays. The novel Progress Monitoring Tool for Eating Disorders (PMED) measure is specifically designed for inpatient settings. Previous research supports the factorial validity and internal consistency of the PMED; however, additional work is needed to determine its appropriateness for complex patient populations. This study used measurement invariance (MI) testing to determine if the PMED administered at programme admission measures the same items in similar ways across patients with anorexia nervosa restricting- and binge-purge subtypes (AN-R; AN-BP) and bulimia nervosa (BN, N = 1121; Mage  = 24.33 years, SD = 10.20; 100% female). Progressively constrained models were used to determine the level of invariance upheld between the three groups. Results indicated that, while the PMED meets configural and metric MI, it does not display scalar invariance. Said otherwise, the PMED similarly assesses constructs and items across AN-R, AN-BP, and BN, however the same score overall may reflect different levels of psychopathology for patients in one diagnostic category versus another. Comparisons of severity between different EDs should be made with caution, however the PMED appears to be a sound tool for understanding the baseline functioning of patients with EDs in an inpatient setting.

One of the earliest indicators of autism spectrum disorder (ASD) is delay in language and social communication. Despite consensus on the benefits of earlier diagnosis and intervention, our understanding of the language growth of children with ASD during the first years of life remains limited. Therefore, this study compared communication growth patterns of infants and toddlers with ASD to growth benchmarks of a standardized language assessment. We conducted a retrospective analysis of growth on the Early Communication Indicator (ECI) of 23 infants and toddlers who received an ASD diagnosis in the future. At 42 months of age, children with ASD had significantly lower rates of gestures, single words, and multiple words, but significantly higher rates of nonword vocalizations. Children with ASD had significantly slower growth of single and multiple words, but their rate of vocalization growth was significantly greater than benchmark. Although more research is needed with larger samples, because the ECI was designed for practitioners to monitor children\'s response to intervention over time, these findings show promise for the ECI\'s use as a progress monitoring measure for young children with ASD. Limitations and the need for future research are discussed.

Monitoring the progress of student learning is an important part of teachers\' data-based decision making. One such tool that can equip teachers with information about students\' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and research, estimating learning progress has relied on approaches that seek to estimate progress either for each student separately or within overarching model frameworks, such as latent growth modeling. Two recently emerging lines of research for separately estimating student growth have examined robust estimation (to account for outliers) and Bayesian approaches (as opposed to commonly used frequentist methods). The aim of this work was to combine these approaches (i.e., robust Bayesian estimation) and extend these lines of research to the framework of linear latent growth models. In a sample of N = 4970 second-grade students who worked on the quop-L2 test battery (to assess reading comprehension) at eight measurement points, we compared three Bayesian linear latent growth models: (a) a Gaussian model, (b) a model based on Student\'s t-distribution (i.e., a robust model), and (c) an asymmetric Laplace model (i.e., Bayesian quantile regression and an alternative robust model). Based on leave-one-out cross-validation and posterior predictive model checking, we found that both robust models outperformed the Gaussian model, and both robust models performed comparably well. While the Student\'s t model performed statistically slightly better (yet not substantially so), the asymmetric Laplace model yielded somewhat more realistic posterior predictive samples and a higher degree of measurement precision (i.e., for those estimates that were either associated with the lowest or highest degree of measurement precision). The findings are discussed for the context of learning progress assessment.

Despite their importance to evidence-based assessment, standardized assessments remain underutilized by mental health practitioners in practice. The underutilization has been attributed to a lack of appreciation of the importance of standardized assessments, lack of knowledge of standardized assessments, and practical barriers to implementation. This study sought to gather the first descriptive data on alcohol and other drug (AOD) practitioners\' attitudes toward, and knowledge and self-reported use of, standardized assessments. Practical barriers to implementation in initial assessment and progress monitoring were also assessed. Ninety-nine Australian AOD practitioners recruited via newsletters of national representative bodies and practitioner networks completed an online survey. While practitioners\' attitudes towards using standardized assessments for initial assessment and progress monitoring were generally positive and consistent with other populations of health practitioners, assessments remained underutilized in practice. Most AOD practitioners did not consider standardized assessments to be feasible to implement. The current findings highlight the importance of practical barriers, particularly organization-level barriers, in the underutilization of standardized assessments in AOD practice. Findings support an extension of dialogue surrounding evidence-based practice beyond treatment selection to include assessment practices at a more general level. The present study offers a starting point from which efforts to improve practitioner compliance with evidence-based best practices can be conceived, designed, and implemented.