Study protocol for transforming health equity research in integrated primary care: Antiracism as a disruptive innovation.

Among the consequences of systemic racism in health care are significant health disparities among Black/African American individuals with comorbid physical and mental health conditions. Despite decades of studies acknowledging health disparities based on race, significant change has not occurred. There are shockingly few evidence-based antiracism interventions. New paradigms are needed to intervene on, and not just document, racism in health care systems. We are developing a transformative paradigm for new antiracism interventions for primary care settings that integrate mental and physical health care. The paradigm is the first of its kind to integrate community-based participatory research and systems science, within an established model of early phase translation to rigorously define new antiracism interventions. This protocol will use a novel application of systems sciences by combining the qualitative systems sciences methods (group model building; GMB) with quantitative methods (simulation modeling) to develop a comprehensive and community-engaged view of both the drivers of racism and the potential impact of antiracism interventions. Community participants from two integrated primary health care systems will engage in group GMB workshops with researchers to 1) Describe and map the complex dynamic systems driving racism in health care practices, 2) Identify leverage points for disruptive antiracism interventions, policies and practices, and 3) Review and prioritize a list of possible intervention strategies. Advisory committees will provide feedback on the design of GMB procedures, screen potential intervention components for impact, feasibility, and acceptability, and identify gaps for further exploration. Simulation models will be generated based on contextual factors and provider/patient characteristics. Using Item Response Theory, we will initiate the process of developing core measures for assessing the effectiveness of interventions at the organizational-systems and provider levels to be tested under a variety of conditions. While we focus on Black/African Americans, we hope that the resulting transformative paradigm can be applied to improve health equity among other marginalized groups.

A Community Data Sharing Resource: The LDbase Data Repository.

The purpose of this invited paper is to show the learning disabilities field what LDbase is, why it's important for the field, what it offers the field, and examples of how you can leverage LDbase in your own work.

Morphological Complexity in Writing: Implications for Writing Quality and Patterns of Change.

PURPOSE: The current study examined (a) the relation between morphologically complex word (MCW) use (words containing at least one derivational morpheme such as prefixes and suffixes) and teachers' ratings of writing quality, (b) average change in MCW use in writing across the school year, and (c) differential change in MCW among students with varying language abilities and linguistic backgrounds including students with developmental language disorders (DLDs) and multilingual learners (MLs). METHOD: Expository writing samples (writing for the purpose of explaining or educating) were collected in October and May from 824 fifth-grade students, including 109 with DLD and 170 who were MLs receiving English as a second language service. Students' written responses were coded for the use of MCW. Pearson product-moment correlations and two-level hierarchical linear models were employed to investigate the association between MCW usage and writing quality, as well as increases in MCW usage over the course of the academic year, taking into account the nested structure of students within classrooms. RESULTS: The relation between students' MCW use and teachers' writing quality ratings was moderately strong (r = .47). Student use of MCW in expository writing showed significant change from fall to spring across all students. However, the amount of change in MCW use across the school year was significantly lower for MLs (effect size [ES] = .09) and students identified with DLD (ES = .10). CONCLUSIONS: The relation between MCW use and teachers' writing quality ratings highlights the utility of MCW as a written language measure for progress monitoring or assessment. The presence of differential change and potential Matthew effects for MLs and students with DLD substantiates the need for further exploration of instructional components that support the increased use of complex vocabulary.

Genetics can inform causation, but the concepts and language we use matters.

Madole & Harden describe how genetics can be used in a causal framework. We agree with many of their opinions but argue that comparing within-family designs to experiments is unnecessary and that the proposed influence of genetics on behavior can be better described as inus conditions.

Teacher Vocabulary Use and Student Language and Literacy Achievement.

PURPOSE: We sought to examine second grade teachers' word use throughout the school day to identify the amount and type of teacher vocabulary use across content areas as well as to examine relationships between this teacher talk and student language and literacy achievement. METHOD: Second grade teachers (n = 64) and a random sample of half of their students (n = 619) participated. Teachers recorded instruction during the school day throughout the year, and students were assessed on vocabulary, grammar, and reading measures in the fall and spring. RESULTS: Findings reveal second grade students hear thousands of words spoken by the teacher each hour of the school day, including more than a thousand different words per hour on average. The large majority of words were the most common words in the English language. On average, there were few academic or curriculum vocabulary words used, but this varied widely between teachers. The proportion of academic words used by teachers during the school day significantly predicted students' end-of-year vocabulary. Teachers who used more academic words had students with higher vocabulary achievement at the end of the school year. There were no other significant relationships between teachers' language and student achievement. CONCLUSIONS: This correlational evidence adds to the existing knowledge of the importance of academic language to student school outcomes and provides implications for further research in the area of academic language at the early elementary level.

Learning and memory deficits produced by aspartame are heritable via the paternal lineage.

Environmental exposures produce heritable traits that can linger in the population for one or two generations. Millions of individuals consume substances such as artificial sweeteners daily that are declared safe by regulatory agencies without evaluation of their potential heritable effects. We show that consumption of aspartame, an FDA-approved artificial sweetener, daily for up to 16-weeks at doses equivalent to only 7-15% of the FDA recommended maximum daily intake value (equivalent to 2-4 small, 8 oz diet soda drinks per day) produces significant spatial learning and memory deficits in mice. Moreover, the cognitive deficits are transmitted to male and female descendants along the paternal lineage suggesting that aspartame's adverse cognitive effects are heritable, and that they are more pervasive than current estimates, which consider effects in the directly exposed individuals only. Traditionally, deleterious environmental exposures of pregnant and nursing women are viewed as risk factors for the health of future generations. Environmental exposures of men are not considered to pose similar risks. Our findings suggest that environmental exposures of men can produce adverse impact on cognitive function in future generations and demonstrate the need for considering heritable effects via the paternal lineage as part of the regulatory evaluations of artificial sweeteners.

Examination of an Automated Procedure for Calculating Morphological Complexity.

PURPOSE: The aim of this study was to advance the analysis of written language transcripts by validating an automated scoring procedure using an automated open-access tool for calculating morphological complexity (MC) from written transcripts. METHOD: The MC of words in 146 written responses of students in fifth grade was assessed using two procedures: (a) hand-coding of words containing derivational morphemes by trained scorers and (b) an automated analysis of MC using Morpholex, a newly developed web-based tool. Correlational analysis between the different MC calculations was examined to consider the relation between hand-coded derivational morpheme counts and the automated measures. Additionally, all MC measures were compared to a previously gathered rating of writing quality to consider predictive validity between the automated Morpholex score and teachers' ratings of writing quality. RESULTS: Automated measures of MC had a strong relation (r = .63) with hand-coding of the number of words with derivational morphemes. Additionally, the number of derivational and inflectional and derivational morphemes accounted for a significant amount of the variation in teachers' overall ratings of writing quality. CONCLUSION: Automated scoring of MC has potential utility as a valid alternative to hand-coding language samples, which may be valuable for progress monitoring of growth in complexity across repeated samples and measuring components that influence perceived quality of academic writing.

The earlier the better: An RCT of treatment timing effects for toddlers on the autism spectrum.

LAY ABSTRACT: Behavioral interventions that incorporate naturalistic, developmental strategies have been shown to improve outcomes for young children who receive an autism spectrum disorder (ASD) diagnosis. Although there is broad consensus that children on the spectrum should begin supports as soon as possible, the empirical evidence for this is relatively limited and little is known about the optimal age to start autism-specific interventions. Our team conducted a randomized controlled trial (RCT) to test the effects of starting intervention at different ages, using the Early Social Interaction (ESI) model, a parent-implemented intervention for toddlers on the spectrum. Participants included 82 autistic toddlers and their caregiver(s) who received 9 months of Individual-ESI and 9 months of Group-ESI, with the timing/order of these two treatment conditions randomized. Thus, families received the more intensive and individualized Individual-ESI at either 18 or 27 months of age. Results revealed that children who received Individual-ESI earlier showed greater treatment gains than those who received this intervention later. Gains were demonstrated in several areas, which included the use and understanding of language, social use of communication skills, and self-help skills. Importantly, these findings were specific to the intensive and individualized parent coaching model compared to group-based treatment, allowing us to rule out the possibility that these timing effects were due to children getting older rather than the treatment itself. Our results suggest that even a narrow window of 18 versus 27 months may have an impact on outcomes and underscore the importance of screening and evaluation as young as possible.

Exploring Individual Differences in Response to Reading Intervention: Data from Project KIDS (Kids and Individual Differences in Schools).

This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 students between five and twelve years old participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442 students collected via parent survey in 2013. All data is currently stored on an online data repository and freely available. Data might be of interest to researchers interested in individual differences in reading development and response to instruction and intervention, as well as to instructors of data analytic methods such as hierarchical linear modeling and psychometrics.

Screening screeners: calculating classification indices using correlations and cut-points.

Given the recent push for universal screening, it is important to take into account how well a screener identifies children at risk for reading problems as well as how screener and sample information contribute to this classification. Picking the best cut-point for a particular sample and screening goal can be challenging given that test manuals often report classification information for a specific cut-point and sample base rate which may not generalize to other samples. By assuming a bivariate normal distribution, it is possible to calculate all of the classification information for a screener based on the correlation between the screener and outcome, the cut-point on the outcome (i.e., the base rate in the sample), and the cut-point on the screener. We provide an example with empirical data to validate these estimation procedures. This information is the basis for a free online tool that provides classification information for a given correlation between screener and outcome and cut-points on each. Results show that the correlation between screener and outcome needs to be greater than .9 (higher than observed in practice) to obtain good classification. These findings are important for researchers, administrators, and practitioners because current screeners do not meet these requirements. Since a correlation is dependent on the reliability of the measures involved, we need screeners with better reliability and/or multiple measures to increase reliability. Additionally, we demonstrate the impact of base rate on positive predictive power and discuss how gated screening can be useful in samples with low base rates.

Assessing Measurement Invariance Across Multiple Groups: When Is Fit Good Enough?

Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation good enough principle, based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and good enough approach. Our results show the approach has potential in combining educational data.

Evaluating Teacher Language Within General and Special Education Classrooms Serving Elementary Students with Autism.

This study examined how teachers and paraprofessionals in 126 kindergarten-second grade general and special education classrooms talked with their 194 students with autism, and further, how individual student characteristics in language, autism symptoms, and social abilities influenced this talk. Using systematic observational methods and factor analysis, we identified a unidimensional model of teacher language for general and special education classrooms yet observed differences between the settings, with more language observed in special education classrooms-much of which included directives and close-ended questions. Students' receptive vocabulary explained a significant amount of variance in teacher language beyond its shared covariance with social impairment and problem behavior in general education classrooms but was non-significant within special education classrooms. Research implications are discussed.

Language Variation in the Writing of African American Students: Factors Predicting Reading Achievement.

Purpose This study aims to examine the predictive relation between measures obtained from African American students' written narrative language samples and reading achievement, as measured by standardized academic assessments. Method Written language samples were elicited from 207 African American students in Grades 1-8. The samples were examined for morphosyntactic variations from standardized written Generalized American English (GAE). These variations were categorized as either (a) specific to African American English (AAE) or (b) neutral across AAE and standardized written GAE (i.e., considered ungrammatical both in AAE and in standardized written GAE). Structural equation modeling was employed to then examine the predictive relation between the density of AAE-specific forms in students' writing and their performance on standardized assessments of literacy and reading vocabulary. This relation was examined while accounting for the density of dialect-neutral morphosyntactic forms, reported family income, age, and written sample length. Results The written samples were highly variable in terms of morphosyntax. Younger students and those from lower income homes tended to use AAE-specific forms at higher rates. However, the density of AAE-specific forms did not significantly predict standardized literacy scores or reading vocabulary after accounting for dialect-neutral variations, income, and sample length. Conclusions These results support the ongoing need to better understand the language, literacy, and overall academic development of students from all backgrounds. It may be essential to focus on dialect-neutral language forms (i.e., morphosyntactic forms that are consistent across both AAE and standardized written GAE) in written samples to maximize assessment validity across students who speak varying dialects of English. Supplemental Material https://doi.org/10.23641/asha.16879558.

Early Detection of Dyslexia Risk: Development of Brief, Teacher-Administered Screens.

Many states now mandate early screening for dyslexia, but vary in how they address these mandates. There is confusion about the nature of screening versus diagnostic assessments, risk versus diagnosis, concurrent versus predictive validity, and inattention to indices of classification accuracy as the basis for determining risk. To help define what constitutes a screening assessment, we summarize efforts to develop short (3-5 min), teacher-administered screens that used multivariate strategies for variable selection, item response theory to select items that are most discriminating at a threshold for predicting risk, and statistical decision theory. These methods optimize prediction and lower the burden on teachers by reducing the number of items needed to evaluate risk. A specific goal of these efforts was to minimize decision errors that would result in the failure to identify a child as at risk of dyslexia/reading problems (false negatives) despite the inevitable increase in identifications of children who eventually perform in the typical range (false positives). Five screens, developed for different periods during kindergarten, Grade 1, and Grade 2, predicted outcomes measured later in the same school year (Grade 2) or in the subsequent year (Grade 1). The results of this approach to development are applicable to other screening methods, especially those that attempt to predict those children at risk of dyslexia prior to the onset of reading instruction. Without reliable and valid early predictive screening measures that reduce the burden on teachers, early intervention and prevention of dyslexia and related reading problems will be difficult.

A Simulation Study on the Performance of Different Reliability Estimation Methods.

The accuracy of certain internal consistency estimators have been questioned in recent years. The present study tests the accuracy of six reliability estimators (Cronbach's alpha, omega, omega hierarchical, Revelle's omega, and greatest lower bound) in 140 simulated conditions of unidimensional continuous data with uncorrelated errors with varying sample sizes, number of items, population reliabilities, and factor loadings. Estimators that have been proposed to replace alpha were compared with the performance of alpha as well as to each other. Estimates of reliability were shown to be affected by sample size, degree of violation of tau equivalence, population reliability, and number of items in a scale. Under the conditions simulated here, estimates quantified by alpha and omega yielded the most accurate reflection of population reliability values. A follow-up regression comparing alpha and omega revealed alpha to be more sensitive to degree of violation of tau equivalence, whereas omega was affected greater by sample size and number of items, especially when population reliability was low.

Data Sharing in Education Science.

Many research agencies are now requiring that data collected as part of funded projects be shared. However, the practice of data sharing in education sciences has lagged these funder requirements. We assert that this is likely because researchers' generally have not been made aware of these requirements and the benefits of data sharing. Furthermore, data sharing is usually not a part of formal training, so many researchers may be unaware how to properly share their data. Finally, the research culture in education science is often filled with concerns regarding the sharing of data. In this article, we address each of these areas, discussing the wide range of benefits of data sharing, the many ways data can be shared, provide a step by step guide to start sharing data, and responses to common concerns.

Trajectories of Response to Treatments in Children with ADHD and Word Reading Difficulties.

This study investigated patterns of response to intervention in children with co-occurring attention-deficit/hyperactivity disorder (ADHD) and reading difficulties (RD), who participated in a randomized clinical trial examining the efficacy of reading intervention, ADHD treatment, or combined treatments. Growth Mixture Modeling (GMM) was used to investigate trajectories of parent and teacher academic impairment ratings and child oral reading fluency, and whether trajectories were predicted by pre-treatment covariates (ADHD severity, reading achievement, phonemic awareness, rapid letter naming, anxiety, oppositional defiant disorder), for 216 children with ADHD/RD in 2nd-5th grade (61.1% male; 72.2% African American; 8.8 ± 1.3 years of age). GMM revealed three trajectories for academic impairment (6.9-24.2% stable, 23.7-78.7% moderately improving, and 14.1-52.1% steeply improving) and oral reading fluency (20.8% low improving, 42.1% moderate improving, and 37.1% high improving). Children in the reading intervention were more likely to be in the stable or moderately improving trajectory than those in the ADHD and combined treatments, who were more likely to be in the steeply improving trajectory for academic impairment. Relative to the ADHD intervention, children in the reading intervention were more likely to be in the high improving trajectory than the moderate or low improving trajectory for oral reading fluency. Children without comorbid anxiety and with better reading skills showed a more positive treatment response for teacher-rated academic progress and oral reading fluency. Results highlight the importance of examining individual differences in response to reading and ADHD interventions. Intervention modality predicted differences in parent/teacher ratings of academic progress as well as reading fluency.

The Relation Between Academic Word Use and Reading Comprehension for Students From Diverse Backgrounds.

Purpose The aims of the current project were twofold: (a) to describe the use of academic words in written language samples by fifth-grade students and (b) to examine the predictive relation between academic word use in academic writing and reading comprehension. Method Investigators utilized written expository responses of 1,128 students in fifth grade who differed in English proficiency and language ability. The sample included 214 students who were English learners (ELs) and 144 students with identified language learning disabilities (LLD). Group differences in the use of academic words from the Coxhead word list were examined. Results ELs and students with LLD used academic words less frequently than their peers and demonstrated less variety in their academic word use. There was a significant relation between students' use of academic words and reading comprehension. Academic word use accounted for 16% of the variance in reading comprehension, which was not significantly different for ELs or students with LLD. The relation was moderated by economic advantage, with the strength of the relation being lower for students who were eligible for free/reduced lunch. Conclusions Findings support the need for additional research on ways to improve academic vocabulary skills to minimize achievement gaps. The relation between academic word use and reading comprehension warrants further consideration.

Open Science in Education Sciences.

The Open Science movement has gained considerable traction in the last decade. The Open Science movement tries to increase trust in research results and open the access to all elements of a research project to the public. Central to these goals, Open Science has promoted five critical tenets: Open Data, Open Analysis, Open Materials, Preregistration, and Open Access. All Open Science elements can be thought of as extensions to the traditional way of achieving openness in science, which has been scientific publication of research outcomes in journals or books. Open Science in education sciences, however, has the potential to be much more than a safeguard against questionable research. Open Science in education science provides opportunities to (a) increase the transparency and therefore replicability of research and (b) develop and answer research questions about individuals with learning disabilities and learning difficulties that were previously impossible to answer due to complexities in data analysis methods. We will provide overviews of the main tenets of Open Science (i.e., Open Data, Open Analysis, Open Materials, Preregistration, and Open Access), show how they are in line with grant funding agencies' expectations for rigorous research processes, and present resources on best practices for each of the tenets.

The Effects of ADHD Treatment and Reading Intervention on the Fluency and Comprehension of Children with ADHD and Word Reading Difficulties: A Randomized Clinical Trial.

Many students with reading difficulties and disabilities (RD) also have Attention Deficit/Hyperactivity Disorder (ADHD). This randomized clinical trial compared the effects of ADHD treatment alone (medication, parent training), intensive reading intervention alone, and their combination on the reading fluency and comprehension of students with both disorders. Students with ADHD and RD were randomly assigned to receive (a) Reading Intervention only (n=74), (b) ADHD Treatment only (n=78), or (c) simultaneous Combined ADHD and RD Treatment (n=70). For phonemic decoding fluency, the Reading Intervention group and the Combined Treatment group both had significantly better outcomes than the ADHD Treatment group, but did not differ from one another. For passage comprehension, the ADHD Treatment group had significantly better outcomes than the Reading Intervention group, while the other groups did not differ from one another. ADHD treatment may support passage comprehension in this population, while fluent decoding is best supported through intensive reading intervention.