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Providing Implementation Supports to Intensify Instruction in an Autism Classroom

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Title: Providing Implementation Supports to Intensify Instruction in an Autism Classroom
Language: English
Authors: Dyer, Kathleen (ORCID 0000-0001-8424-3242), Redpath, Caroline
Source: Psychology in the Schools. Jun 2021 58(6):1041-1055.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 15
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Descriptors: Program Implementation, Autism, Pervasive Developmental Disorders, Students with Disabilities, Skill Development, Language Skills, Speech Skills, Goal Orientation, Consultation Programs, Feedback (Response), Reinforcement, Instructional Effectiveness
DOI: 10.1002/pits.22486
ISSN: 0033-3085
Abstract: This study evaluated whether an implementation support package using collaborative goal setting, nondirective consultation, feedback, and reinforcement would result in an increase in the implementation of planned speech and language programs in a classroom for students with autism spectrum disorders. Additional measures were collected to assess generalization to other skill-building programs in the students' schedules that were not targeted in the intervention. The results of a multiple-baseline analysis revealed that the implementation supports were successful in increasing the frequency of implementation of speech and language programs to goal levels. Further, these gains were maintained when staff support was faded to a weekly schedule for two of the students. The third student required weekly staff support to maintain goal levels. Discontinuation of direct staff support in the maintenance phase resulted in higher and more consistent implementation frequency than seen in the baseline. Similar trends in the generalization targets were evidenced. The results are discussed in relation to implementation science for students with autism.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1294249
Database: ERIC
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  Value: <anid>AN0150131653;pis01jun.21;2021May06.05:40;v2.2.500</anid> <title id="AN0150131653-1">Providing implementation supports to intensify instruction in an autism classroom </title> <p>This study evaluated whether an implementation support package using collaborative goal setting, nondirective consultation, feedback, and reinforcement would result in an increase in the implementation of planned speech and language programs in a classroom for students with autism spectrum disorders. Additional measures were collected to assess generalization to other skill‐building programs in the students' schedules that were not targeted in the intervention. The results of a multiple‐baseline analysis revealed that the implementation supports were successful in increasing the frequency of implementation of speech and language programs to goal levels. Further, these gains were maintained when staff support was faded to a weekly schedule for two of the students. The third student required weekly staff support to maintain goal levels. Discontinuation of direct staff support in the maintenance phase resulted in higher and more consistent implementation frequency than seen in the baseline. Similar trends in the generalization targets were evidenced. The results are discussed in relation to implementation science for students with autism.</p> <p>Keywords: autism; implementation science; implementation supports</p> <hd id="AN0150131653-2">INTRODUCTION</hd> <p>Research indicates that children with autism spectrum disorders (ASD) need to be actively engaged in intensive instructional programming (Eldevik et al., 2019; National Autism Center, 2009; Wong et al., 2015). It has been recommended that model programs provide 20–45 h of intervention per week. Furthermore, it has been suggested that intensity also be thought of in the context of a high frequency of opportunities to respond actively to skill‐building programs (National Research Council, 2001; Strain & Hoyson, 2000). That is, students with severe and persistent learning challenges such as those presented by students with ASD might require 10–30 times more opportunities to practice than their typically developing peers to effectively learn a skill (Fovel, 2013; Gersten et al., 2008; Howard et al., 2005). Despite this evidence, there is a critical concern that interventions are not efficaciously implemented by educators providing services to these children in public systems (Dingfelder & Mandell, 2011; National Research Council, 2001).</p> <p>Thus, while there is a need for evidenced‐based systems to monitor and increase implementation frequency for children with ASD in school settings, there is a lack of research in this area. Wilkerson (2007) suggests that implementation intensity be evaluated by examining the permanent products that are generated as a result of an intervention and evaluated to determine the extent to which a corresponding component was implemented in the form of summarized performance data. This monitoring method could be an accurate measure of implementation frequency as it is efficient and less reactive than direct observation methods. Additionally, the National Center on Intensive Intervention (2103) recommends, not only a monitoring system where intervention intensity is tracked, but a process where the team identifies barriers to implementation, including the need for areas staff training and support.</p> <p>Likewise, Fixsen et al. (2005) state that systematic and ongoing supports for educators are needed for the implementation of interventions. They further assert that passive "train and hope" strategies, such as disseminating the information through materials and in‐service workshops, are ineffective. In their study to investigate teacher perspectives on implementation, Greenway et al. (2013) found that teachers of students with developmental disabilities, including those with ASD, reported several barriers to implementation. These included reduced access to oversight and supports, as well as diminished accountability to the school district administration. Some teachers reported that collaborative conversations and specific assistance from consultants and experts would be most helpful.</p> <p>Sanetti and Collier‐Meek (2015) provided this type of assistance to teachers in regular education classrooms. They found that research‐based staff supports, including direct training, development of solutions to address barriers, prompts, and modeling, resulted in higher levels of correct treatment implementation. Further, increased staff support was needed before some teachers were able to deliver the interventions as planned. Similarly, Odom et al. (2019) suggest that one area that holds promise is the newly emerging field of implementation science, where research‐based performance management strategies can be used to promote evidenced‐based practices for children and youth with ASD. Implementation support strategies that have been successful in producing improved performance of instructors for children with autism and other developmental disabilities in service‐delivery settings include goal setting, instruction, modeling, rehearsal, performance feedback, coaching, nondirective consultation, preferences, and reinforcement (DiGennaro Reed & Reed, 2014; Kucharczyk et al., 2012; Parsons et al., 2012; Peck et al., 1989). These strategies have proven effective in increasing staff implementation of evidenced‐based interventions such as discrete trial training (Dib & Sturmey, 2007), natural language training techniques (Dyer & Karp, 2013), functional activities (Dyer et al., 1984), augmentative and alternative communication (AAC) instruction (Wood et al., 2007) and writing of Individualized Education Program (IEP) objectives (Ruble et al., 2010).</p> <p>In our own school setting, monthly clinical reviews of data collected by classroom staff revealed that skill‐building programs were not being implemented on a consistent basis. That is, they were not being implemented frequently, with repeated opportunities on a daily basis. Rather, they were being implemented at a much lower rate, with ranges averaging approximately 20%–60% of the week (e.g., 1–3 days). The Clinical Director provided feedback to the classroom teacher and other members of the clinical team regarding these deficient levels during the months that immediately preceded the study, but problems with intervention intensity persisted.</p> <p>This problem was particularly concerning with regard to the frequency of implementation of speech and language programs. Due to the critical importance of addressing the core deficit of communicative competence in learners with ASD (National Research Council, 2001), the development of speech and language was a primary emphasis of the school program. Challenging behaviors such as self‐injury, aggression, and tantrums are known to develop in children with ASD in lieu of conventional means of communicating, and teaching functional communication skills as a means to treat these serious behaviors was a focus of the speech and language department.</p> <p>In summary, the current research literature provides evidence that the provision of staff support results in improved treatment implementation by classroom staff. We therefore hypothesized that an implementation support package for the direct classroom staff, including collaborative goal setting, nondirective consultation, feedback, and reinforcement, would result in increased frequency of implementation of speech and language programs. Two sets of data were monitored as dependent variables. The first set were data reflecting the frequency of implementation of speech and language programs, and for this set, staff received the implementation supports. The second set of data comprised the remaining skill‐building programs in the students' daily schedules. These data were measured to assess the impact of potential treatment impact to these programs. That is, this measure assessed whether the exclusive focus on speech and language programs would result in (a) a decrease in implementation of the other skill‐building programs due to the decreased time, (b) no change in frequency of implementation of other programs, or (c) a generalized increase in program implementation across all skill‐building programs. In addition, we were concerned that classroom staff completing the summarized performance data had the potential to be influenced by social desirability and may have been motivated to inflate the reported frequency of program implementation in the summarized performance data during the support program. Thus, this second set of measures also served as a "reactivity check." The staff were unaware that this second set of data was being monitored.</p> <p>To explore these issues with direct staff in our school classroom for students with ASD, two questions were posed in this study: (1a) Will the provision of a research‐based staff support package result in an increase in the frequency of implementation for speech and language programs? (1b) If so, would the frequency of implementation for other skill‐building programs be changed? and (<reflink idref="bib2" id="ref1">2</reflink>) Will the classroom staff continue to implement programs with a high frequency when levels of staff support are faded?</p> <hd id="AN0150131653-3">METHODS</hd> <p></p> <hd id="AN0150131653-4">Participants and setting</hd> <p>The site of this study was a full‐day full‐year intensive behavioral intervention school for children with ASD aged 3–15 years old. All students were referred to this separate self‐contained special education school by their home districts. The students required special education services due to barriers in learning, communication, and interfering problem behaviors such as aggression, self‐injury, and stereotyped behavior. All of the students were receiving special education services via an IEP under the educational classification of autism. This school implemented interventions based on research findings conducted with children diagnosed with ASD. These included teaching through discrete trial interventions; naturalistic teaching procedures; task‐analyzed chaining procedures; embedding instruction in functional routines; discrimination training; and prompt fading procedures, with a strong emphasis on the generalization of skills across school, home, and community environments. For managing challenging behavior, function‐based interventions, based on assessment information gathered through functional behavior assessment, were implemented.</p> <p>Because a major agency objective included generalization across adult instructors, children typically worked with three–five different Associate Instructors (AIs) during a regular school day. In addition, to maintain full‐day instruction, there were periodic changes in staffing due to turnover and occasional substitute instructors. Of the nine classroom staff who regularly participated, all held bachelor's degrees, and one staff was a certified special education teacher. Each of the instructors who participated in this study were regular employees of the school and had received training to implement evidenced‐based intervention and data collection for students with ASD (cf. Sulzer‐Azaroff et al., 2012). Specifically, staff first attended a 2‐day in‐service training on fundamental principles and procedures for teaching children with ASD using behavioral intervention procedures. Then, staff were required to perform core competencies, including how to correctly implement discrete trials, preference and choice, prompt fading, shadowing, data collection, and behavior support procedures. These skills were trained through modeling, role‐playing, and in vivo feedback until mastery was achieved on competency‐based checklists administered by certified staff. In addition, all staff were required to pass examinations indicating mastery of readings in evidenced‐based assessment and treatment for children with autism (Bondy & Frost, 2003; Demchak & Bossert, 2005; Leaf & McEachin, 1999; Luce & Smith, 2007; Sulzer‐Azaroff et al., 2012).</p> <p>The program implementation data (see dependent variables below) for three young male students were targeted. Fred, a 6‐year‐old, Ned, a 7‐year‐old, and Cade, a 6‐year‐old, were all diagnosed with ASD by a licensed psychologist independent of their current educational facility. These students attended the school for 30 h per week.</p> <p>The Verbal Milestones Assessment and Placement Program (VB‐MAPP; Sundberg, 2008) was administered to each child in the school as a regular part of the school assessment battery. This provided a comprehensive criterion‐referenced assessment curriculum guide and skills tracking system designed specifically for children with autism and other individuals who demonstrate language delays. Both the VB‐MAPP Milestones Assessment and the VB‐MAPP Barriers Assessment were used. The Milestones Assessment provided a representative sample of common language and social skills milestones, and the Barriers Assessment provided an assessment of common learning and language acquisition barriers faced by children with ASD.</p> <p>According to the VB‐MAPP Milestones Assessment, Fred's scores ranged from 18 to 48 months. His strengths were in the areas of labeling, listener skills, visual perception, reading, and early math skills. According to the VB‐MAPP Barriers Assessment, severe language and learning obstacles included behavior problems, defective social skills, and reinforcer dependency. Ned received VB‐MAPP Milestone scores that scattered up to the 48‐month level. His strengths were in the areas of requesting, labeling, visual–perceptual skills, play, and early reading skills. According to the VB‐MAPP Barriers Assessment, severe language and learning obstacles included behavior problems, instructional control, weak social skills, reinforcer dependency, and hyperactive behavior. Cade received VB‐MAPP Milestone scores that were relatively consistent at the 18–30 months level, with some scatter up to 48 months. His strengths were in the areas of visual–perceptual skills, early reading skills, and early math skills. According to the VB‐MAPP Barriers Assessment, severe language and learning obstacles included impaired social skills, prompt dependency, failure to generalize, weak or atypical motivating operations, reinforcement dependency, self‐stimulation, failure to make eye contact, or attend to people and sensory defensiveness.</p> <p>These students were selected because observations of their summarized performance data revealed that skill‐building programs were not being implemented on a consistent day‐to‐day basis. While the classroom teacher and clinical team had been repeatedly informed of this problem during these formal reviews, no meaningful changes were observed in program implementation over the previous school year.</p> <hd id="AN0150131653-5">Dependent variables</hd> <p>Each child had short‐term objectives mandated by their IEP. The number of short‐term objectives varied across children, with an average of 32 objectives (range 24–37). For each short‐term objective, a program was written that specified the instruction and data collection, and was written in a specific format. Each program included the target behavior, the behavioral objective, specifying the context in which the behavior occurred and a specific description of the behavior; the instructional cue(s), the teaching guide, specifying instructional delivery procedures; the generalization procedures; the mastery criterion; the set items; the data collection system. The purpose of the written program was to facilitate consistency of treatment delivery across instructors and to provide a record of specific programs and instructions that the child received. When the program was implemented, all instructors collected trial‐by‐trial student correct response data on daily data collection sheets. All instructors' implementation of programs contributed to calculating program completion. At the end of the day, the final scores were entered onto a weekly summary sheet. All data for this study were collected from these weekly summary sheets.</p> <p>The primary dependent variable was <emph>Program Implementation for Targeted Speech and Language Programs</emph>, defined as the percentage of speech and language programs recorded as implemented per week. These summarized performance data were determined as follows: Each day, the instructors entered the summarized correct response data on a "Weekly Data Summary Sheet," reflecting either total percent correct or frequency reflecting that child's performance for that day (cf. Fovel, 2013). Data were scored as a correct program implementation data point if it had a minimum of five trials or occurrences that were summarized on the data collection form. These data were collected through visual inspection of each child's weekly datasheet. The percent program implementation was calculated with the formula: number of programs completed divided by number of opportunities to implement the program in a school week and multiplied by 100%. For example, if two speech and language programs were planned to be completed on a daily basis for a 5‐day week, there would have been 10 opportunities for the staff to implement the program. If each program had been implemented twice that week, the percentage program implementation would have been 40%. A "weekly data point" typically consisted of 5 school days. However, shorter weeks did occur throughout the year, due to child absences, or snow days, or holidays.</p> <hd id="AN0150131653-6">Nontargeted programs</hd> <p>The <emph>implementation of the remaining programs</emph> in each student's daily schedule was also scored and calculated in the same matter as the speech and language programs as described above. There were two reasons for collecting this measure. First, we evaluated whether the provision of support to the staff implementing the speech and language programs would result in a change in the implementation of other programs. This second set of data also served as a "reactivity check," due to the fact that the staff were unaware that these data were being monitored, and did not receive any feedback or other positive contingencies for data collection. That is, if the staff were aware that the speech and language programs were being monitored, they might react by artificially inflating speech and language scores. Thus, such a pattern in the data might reflect staff reactivity.</p> <p>Interobserver agreement was collected by the authors for the percent program implementation for 31% of randomly selected weekly datasheets across each experimental condition for each student. Interobserver agreement was calculated on a point‐by‐point basis using the formula of agreements divided by disagreements plus disagreements, multiplied by 100%. Overall agreement averaged 98% (range 86%–100%).</p> <hd id="AN0150131653-7">Design and procedure</hd> <p>The current study used a multiple‐baseline design across data sets. For Cade, an ABA reversal was also implemented (Bailey & Burch, 2002).</p> <hd id="AN0150131653-8">Baseline</hd> <p>During the baseline condition, the weekly summary datasheets were collected and scored unobtrusively, and instructors carried out their regular job responsibilities, which was to implement the child's IEP programs, and monitor progress through daily data collection. In addition, the Clinical Director continued to conduct monthly clinical reviews where the clinical team reviewed the child's current educational programs, progress, and the extent to which the programs were delivered correctly. The classroom teacher and clinical team (Behavior Analyst, Speech‐Language Pathologists, Occupational Therapist, and Physical Therapist) attended the reviews, and were provided with feedback on frequency of program implementation. These reviews were a regular part of the school protocol and continued throughout all phases of the study.</p> <hd id="AN0150131653-9">Implementation support package‐phase 1</hd> <p>Research‐based implementation supports were provided by the Clinical Director (Kathleen Dyer), who had extensive experience and understanding of the processes of implementation in the school and classroom where the study was conducted. This package provided a higher quality of support than the review process in three respects: (<reflink idref="bib1" id="ref2">1</reflink>) the meetings increased from monthly to weekly; (<reflink idref="bib2" id="ref3">2</reflink>) the addition of all direct instructional classroom staff, including the AIs, in the meetings; and (<reflink idref="bib3" id="ref4">3</reflink>) the use of a systematic research‐based staff support package with the exclusive focus on increasing the frequency of program implementation. This implementation support package consisted of five components: (<reflink idref="bib1" id="ref5">1</reflink>) providing a rationale; (<reflink idref="bib2" id="ref6">2</reflink>) collaborative goal setting; (<reflink idref="bib3" id="ref7">3</reflink>) nondirective consultation; (<reflink idref="bib4" id="ref8">4</reflink>) weekly feedback; and (<reflink idref="bib5" id="ref9">5</reflink>) staff identified preferred tangible rewards.</p> <p>At the beginning of the intervention, the Clinical Director met with all of the classroom staff during their weekly classroom meeting. That is, in addition to the teacher and clinical team (who attended the clinical reviews), the direct classroom staff, the AIs, were included in the meetings. These AIs were responsible for implementing the students' programs on a day‐to‐day basis in the classrooms. During all of the meetings, the Clinical Director used collaborative, nondirective verbal, and nonverbal communication strategies (cf. Kucharczyk et al., 2012; Peck et al., 1989). These included using open‐ended questions (e.g., "What do you think are the barriers to running this program?", "How do you think we could increase implementation of his speech programs during the day?"). Nonverbal strategies included focusing on the speaker, using active listener behaviors, and nonverbal encouragers (e.g., nodding head, smiling, maintaining eye contact, taking notes).</p> <p>During the first classroom meeting, the Clinical Director provided a rationale for the intervention by discussing the organization's mission and vision of providing high‐quality education through intensive programming, and the value of program implementation and providing frequent, high rates of opportunities to respond. To set a benchmark goal level of program implementation, input was recruited from the AIs, the teacher, the behavior analyst, and the speech‐language pathologist. The consensus was that targeted goal criterion of 80% of program implementation per program, per week was appropriate and would be beneficial to student learning. In addition, all staff agreed that each student should be provided at least five opportunities for the goal to be addressed per day to consider the program as having been implemented.</p> <p>For each of the three children's program implementation data, the first phase of the intervention consisted of the following implementation support components:</p> <p></p> <ulist> <item> Reviewing baseline program implementation data for the targeted child by distributing copies of weekly summary sheets to staff with data for targeted programs highlighted.</item> <p></p> <item> Discussing programs that were not implemented frequently, and asking what support was needed to increase program implementation.</item> <p></p> <item> Discussing possible barriers to program implementation including:</item> <p></p> <item> o Interfering behavior problems</item> <p></p> <item> o Lack of sufficient time during the school day to implement the program</item> <p></p> <item> o Lack of clarity on how to implement the program</item> <p></p> <item> o Other barriers as described by staff.</item> <p></p> <item> Using nondirective consultation strategies by inviting the staff to develop solutions to reduce these barriers, and offering ideas to the staff to increase implementation, including:</item> <p></p> <item> o Highlighting programs that had low implementation on the student's datasheet at the end of each day to serve as a cue for staff the next day</item> <p></p> <item> o Scheduling a specific scheduled time to implement the program</item> <p></p> <item> o Getting more training on how to teach the new behavior</item> <p></p> <item> o Clarifying the written lesson plan</item> <p></p> <item> o Ensuring materials are readily available for all lessons.</item> <p></p> <item> Creating an action plan to increase the implementation that was developed and agreed upon by all staff.</item> <p></p> <item> Creating a contract with the classroom staff of a group reward of an hors d'oeuvres (e.g., nachos, pizza) for the staff contingent upon 2 weeks at the goal level of 80% program implementation following a preference assessment (hors d'oeuvres at the end of the day classroom meeting had a history of being preferred by this classroom).</item> <p></p> <item> Reviewing the program implementation data at each weekly class meetings.</item> <p></p> <item> Providing performance feedback in the form of praise for increased implementation.</item> <p></p> <item> Posting visual feedback in the classroom of data and praise for increased performance.</item> </ulist> <hd id="AN0150131653-10">Implementation support package‐phase 2</hd> <p>Once program implementation data were stable at the goal level, the primary reinforcement component (hors d'oeuvres for classroom staff), was removed. The Clinical Director continued to provide weekly feedback to the classroom staff on the percent program implementation, and discuss barriers and solutions to improving implementation that had fallen below the 80% level.</p> <hd id="AN0150131653-11">Implementation support package‐phase 3</hd> <p>After stability was obtained in program implementation data for 2 weeks in Phase 2, the same implementation supports used in Phase 2 were provided, but on a faded schedule of once every 2 weeks. If stability was not maintained, Phase 2 was re‐instated, and weekly supports were again provided until the criterion rates were re‐mastered (see results for Cade).</p> <hd id="AN0150131653-12">Maintenance‐phase 4</hd> <p>After high and steady levels of targeted program implementation was obtained for all three students, implementation supports during the weekly classroom meetings ended, and the Clinical Director continued the regularly scheduled reviews that were conducted as a regular part of the school protocol (see Section 1.3.1 above). The classroom staff were informed that implementation supports during the classroom meetings was ending.</p> <hd id="AN0150131653-13">Maintenance‐phase 5</hd> <p>Program implementation data continued to be collected unobtrusively for 5 weeks following the termination of the intervention.</p> <hd id="AN0150131653-14">Acceptability measures</hd> <p>The staff were asked to anonymously rate the intervention according to a 5‐point scale, with a score of 1 (<emph>strongly disagree</emph>) to 5 (<emph>strongly agree</emph>) with respect to the following statements:</p> <p></p> <ulist> <item> 1. The project to improve program implementation was important for student learning.</item> <p></p> <item> 2. The classroom program implementation data improved as a result of the project.</item> <p></p> <item> 3. The class meetings to discuss program implementation data were helpful.</item> <p></p> <item> 4. The feedback and rewards were helpful.</item> <p></p> <item> 5. Setting the 80% goal helped improve the program implementation data.</item> </ulist> <p>The staff were also invited to provide additional feedback in the form of open‐ended written comments. After the ratings were submitted, the ratings were added together and divided by the total number of responses to arrive at a mean rating for each statement.</p> <hd id="AN0150131653-15">RESULTS</hd> <p></p> <hd id="AN0150131653-16">Program implementation data for targeted speech and language programs</hd> <p>Figure 1 shows that the targeted program implementation data for the speech and language programs (closed circles) increased for all three students. The top graph shows the summarized weekly data for Fred. During baseline, the program implementation data for targeted speech and language programs was highly variable, ranging from 28% of the opportunities to implement the program to 93%, with an average of 52.5% program implementation across 10 weeks of data collection. When all of the implementation supports were provided in Phase 1, goal levels of 89% and 83% of targeted speech‐language pathology (SLP) program implementation were achieved within 3 weeks. When supports were faded to weekly feedback during Phase 2, goal levels were maintained, with an average of 88% across 2 weeks. These high levels sustained at an average of 89% (range 50%–100%) when supports were faded to bi‐weekly feedback for 25 weeks in Phase 3. Three data points fell below goal levels, and these decreases did not persist. During the maintenance phase, the student's data averaged 87% with the last data point at 75%. Similar results are shown for the targeted speech and language program implementation for Ned, whose data showed increases to goal levels in two sessions, and continued high and steady levels of program implementation, averaging 95% when support was faded to bi‐weekly levels. During the maintenance condition, program implementation averaged 87%.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01jun21/pits22486-fig-0001.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="pits22486-fig-0001.jpg" title="1 Percentage of targeted speech and language programs completed (circles) and nontargeted skill‐building programs completed (squares)" /> </p> <p></p> <p>Cade's program implementation data was more variable. The data show some increases in baseline levels over the 20 weeks of baseline, with an average of 47% for the first 2 months of baseline, and an average of 76% in the last 2 months of baseline. It should be noted that while program implementation for Cade increased during baseline, these increases stabilized for 9 weeks, and the goal level of 80% program implementation across two consecutive weeks was never achieved during this period. Therefore, the support system was instated for Cade after Fred and Ned exhibited increased and steady levels of program implementation at goal levels. During Phases 1 and 2, program implementation increased to goal levels for Cade. Because stability in goal levels was not maintained during the Phase 3 bi‐weekly feedback condition, and data were showing a decreasing trend, Phase 2 weekly feedback was re‐instated until data returned to high levels. When performance management strategies were removed during the maintenance condition, Cade's data decreased to an average of 69% (range 60%–80%).</p> <hd id="AN0150131653-18">Program implementation data for nontargeted programs</hd> <p>Figure 1 shows that the generalization program implementation data, represented by the closed squares, increased for all three students. The top tier of the graph shows the summarized weekly data for Fred. During baseline, the generalization implementation trends closely corresponded to the increases observed in the targeted SLP programs but at lower levels. This effect continued throughout treatment conditions. Overall rates increased from an average of 37% (range 19%–52%) during baseline to an average of 67.6% (range 32%–85%) during treatment. These gains continued to increase during the maintenance phase with an average of 73% (range 70%–76%). Similar results were also seen for the remaining two children, with their generalization data closely corresponding to levels seen in the baseline, treatment, and maintenance conditions, but at slightly lower levels.</p> <hd id="AN0150131653-19">Acceptability measures</hd> <p>Table 1 displays the results of the acceptability questionnaire. The average ratings from the classroom staff showed that in general, the staff rated the intervention as important for student learning, and that the program implementation data improved as a result of the project. The mean ratings for components of the intervention were also high, but the ranges of responses showed more variability, with staff perceiving the class meetings as the most helpful (mean 4.5, range 4–5), followed by goal setting (mean 4.5, range 2–5), and feedback and rewards (mean 4, range 1–5).</p> <p>1 TableThe mean and range of responses from the acceptability questionnaire</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th /><th align="left">Degree of agreement with statement</th></tr><tr valign="bottom"><th>Statement</th><th /><th align="left">Mean</th><th align="left">Range</th></tr></thead><tbody valign="top"><tr><td>1.</td><td>The project to improve program implementation was important for student learning.</td><td>4.8</td><td>4–5</td></tr><tr><td>2.</td><td>The classroom program implementation data improved as a result of the project.</td><td>4.5</td><td>3–5</td></tr><tr><td>3.</td><td>The class meetings to discuss program implementation data were helpful.</td><td>4.5</td><td>4–5</td></tr><tr><td>4.</td><td>Setting the 80% goal helped improve the program implementation data.</td><td>4.5</td><td>2–5</td></tr><tr><td>5.</td><td>The feedback and rewards were helpful.</td><td>4.0</td><td>1–5</td></tr></tbody></table> </ephtml> </p> <p>The comments from the staff were all positive in nature, and included the following:</p> <p></p> <ulist> <item> "Outstanding results. Collaborative discussions regarding obstacles/barriers to achieving high program implementation and input/problem solving was very effective."</item> <p></p> <item> "Appreciated the feedback and brainstorming on why program implementation was down and how to improve it. Look forward to more improvement ideas in the future to ensure our students are reaching their individual potentials and we are supporting and encouraging it."</item> <p></p> <item> "It was nice to see overall implementation improve. Would like to see this project be spread throughout the building. Would you go back in 2 or 3 months to see if it maintained?"</item> <p></p> <item> "Get to the rest of the building with program implementation—Room 4 sets the standards!"</item> </ulist> <hd id="AN0150131653-20">DISCUSSION</hd> <p>Research‐based support for direct classroom staff, including goal setting, nondirective consultation, feedback, and reinforcement, was successful in attaining goal levels of implementation intensity for targeted speech and language programs. For Fred and Ned, goal levels were maintained when staff support was faded to a bi‐weekly schedule. For Cade, a weekly schedule of staff support was required for implementation intensity to remain at goal levels. Discontinuation of direct staff support in the maintenance phase resulted in slight decreases in implementation frequency for all students. However, these levels during maintenance were higher and more consistent than the baseline. Similar improvements were seen in the nontargeted skill‐building programs consistent with the introduction of staff support.</p> <p>The study extends the literature on treatment integrity for students with autism (DiGennaro Reed et al., 2013; Parsons et al., 2012; Thomson et al., 2009), by targeting implementation frequency, which is a facet treatment of integrity that has heretofore not been addressed in the research literature. The results can be discussed in relation to several issues. First, this study used a package of support strategies that are consistent with core implementation components that are recommended by the National Implementation Research Network (Fixsen et al., 2005), which include staff selection, preservice and in‐service training, ongoing consultation and coaching, staff and program evaluation, facilitative administrative support, and systems interventions. These authors noted that these components are combined to maximize their influence on staff behavior and are integrative and compensatory, such that one cannot be isolated from another.</p> <p>In the current study, the weekly support in the form of team collaboration with all the staff is consistent with the recommendation to provide ongoing coaching and administrative support. As such, it is interesting to consider the results in relation to the possible effects of this team collaboration. Before the onset of the study, the class teacher and clinical team had been regularly advised of the need for a high frequency of program implementation during monthly reviews of student data. However, baseline assessments of weekly data summaries revealed the need for increased frequency of implementation using research‐based staff support strategies. Including the direct AI staff in the efforts to increase program implementation might have been a key variable, as they were the staff who identified obstacles to the frequency of program implementation that were not readily apparent to the teacher or clinical team members. For example, the AIs voiced concerns that Cade was spending too much time on his iPad, because he gained contingent access to his device for 2 min after earning every five tokens. After an action plan was put in place to decrease contingent access to the iPad, the frequency of program implementation increased the following week. It is therefore suggested that future research efforts more specifically isolate the variable of including direct classroom staff in efforts to improve implementation.</p> <p>Fixsen et al. (2005) also suggest that the qualities of the individual providing "administrative facilitation" is an important factor in the success of implementing change, and recommend that the individual be knowledgeable about the program, use a collegial style and be a strong communicator. The Clinical Director in this study was the founder of the school and had extensive experience with each student's programs, the capabilities of staff, and the overall day‐to‐day operation. The staff were guided, using a nondirective style, to develop their own reasons why increasing implementation frequency was important. This may have served to bring the staff on board with the need to boost implementation without creating coercion to do so. During the weekly meetings, the staff expressed statements guiding their own behavior to boost implementation, such as, "We should do this for all the programs."</p> <p>This and the positive role of team collaboration were reflected in the acceptability surveys, where the staff rated the class discussions as the most helpful treatment component, and provided comments such as "appreciated the feedback and brainstorming on why program implementation was down and how to improve it," and "collaborative discussions regarding obstacles/barriers to achieving high program implementation and input/problem solving was very effective." This finding is consistent with recommendations that the direct staff should be invited to help identify barriers to the implementation of evidenced‐based practices (Greenway et al., 2013; Vollmer et al., 2008). Indeed, over the course of the study, the classroom staff identified multiple obstacles to the frequency of program implementation, including interfering behavior problems; access to distracting "calming" toys; lack of clarity on how to implement the programs; and student difficulty with making progress on the programs. These findings suggest that efforts to increase the frequency of program implementation involve a complex set of variables that require collaborative staff troubleshooting. One such variable might be the total number of objectives on an IEP. Due to the applied nature of this intervention, we were unable to control for the number of objectives, with one student receiving programming for 37 objectives. It is suggested that future investigations consider the impact of the volume of objectives on the frequency of program implementation across all objectives.</p> <p>Second, the generalization of the treatment effect to the other nontargeted programs in each student's schedule is another interesting result. At the onset of the study, one concern was that exclusive focus on increasing the frequency of implementation of speech and language programs might result in a corresponding decrease in the implementation of other skill‐building programs on the student's schedule. In fact, the opposite happened, as there was a corresponding increase in frequency of implementation of the nontargeted programs that was functionally related to the intervention. Thus, treatment focus on the subset of speech and language programs had a generalized effect on the remaining nontargeted skill‐building programs. This generalized effect might have had an impact on Cade's program implementation data as well. Specifically, while the first 2 months of baseline averaged 47% program implementation for targeted speech and language programs and 48% for the nontargeted skill‐building programs, the last 9 weeks of baseline, while stable and slightly variable, showed an increase to 77% for targeted and 58% for nontargeted programs. This suggests that the newly acquired practice of increasing the frequency of speech and language programs may have resulted in a larger response class of increasing frequency for all the students' skill‐building programs. When queried about the generalized treatment effect, one staff responded that she had regularly urged the classroom staff in this direction. Further evidence of increased perceived value of program accountability was reflected in the staff comments that this system should be implemented school‐wide, and that they were proud of the result ("Room 4 sets the standards!").</p> <p>As mentioned above, when support was faded to a bi‐weekly schedule during Phase 3, gains maintained for two of the three student's program implementation data. When decreases in the frequency of program implementation were seen when staff support was faded for Cade's programs, weekly support was reinstated and goal levels of program implementation returned. A similar type of effect was seen by Sanetti and Collier‐Meek (2015) in their study on multi‐tiered systems of support for teachers implementing behavior support plans. Specifically, when their data revealed lower levels of treatment implementation, these researchers found that increased implementation supports were effective in improving levels of treatment delivery. In the current study, a review of our student profiles were re‐examined to determine if there were any characteristics that contributed to the increased need for staff implementation supports for Cade. It is interesting to note that Cade displayed higher levels of learning and language acquisition obstacles, as reflected on his VB‐MAPP Barriers assessment scores, than the other two students. Specifically, he demonstrated behaviors that severely competed with on‐task behavior, including excessive self‐stimulation, failure to attend to people and sensory defensiveness. As such, the staff commented that it took longer to implement his programs due to the amount of time it took to gain and maintain his focus and attention to the programs. Thus, while planning systems of implementation support for staff, student characteristics might need to be considered as another important variable.</p> <p>Wilkerson (2007) suggested that the intensity of implementation be measured indirectly by administrators examining summarized performance data. Thus, while the monitoring method used in this study most likely reduced staff reactivity to direct administrator classroom presence, a limitation of this method is that program implementation measures were not a directly verified measure of child engagement. In addition, this indirect measure, still had the potential to be subject to staff bias and reactivity in completing the datasheets. However, given that there was continued variability in the data, especially Cade's, in the intervention condition, makes it less likely that staff artificially inflated the program implementation data. In addition, treatment effects were noted in the nontargeted program data that were collected without staff knowledge, and all data remained above baseline levels when staff were informed that the intervention had ended. It is important to note that during all phases of this investigation, regular treatment integrity checks to verify child progress were conducted through direct observation of the staff implementing programs with the students during the clinical reviews. It is therefore recommended that verification of treatment implementation and child progress through direct observation be an essential component of any program implementation evaluation system that utilizes a review of data.</p> <p>Another limitation of this study was that the lead author was also the lead interventionist, and thus could hold bias with regard to the experimental outcomes. A number of measures were taken to control for this possible confound. First, the Clinical Director was present in all phases of the investigation, and as such, rules out a functional relation between the presence of the Clinical Director and a treatment effect. Second, as described previously in the method, the Clinical Director was attempting to gain increases in the frequency of program implementation before the initiation of the study during the clinical reviews. As such, this was an ongoing administrative, rather than research‐oriented objective. Third, the Clinical Director utilized the multiple‐baseline design to determine the effectiveness of this administrative support, and the frequency of program implementation improved when and only when the staff were provided with the research‐based support package. Fourth, the inclusion of a second observer to gain interobserver agreement reduces bias and reactivity in the measurement of the dependent variables. Finally, the fact that the program implementation for Cade was more variable and required more staff support than was originally hypothesized, adds more evidence that the research outcomes were not influenced by experimenter bias.</p> <p>While the current study could have been strengthened by direct observation and measurement of implementation in the classroom by two independent observers, the school setting did not have personnel budgeted and contracted for this study function. Further, the aim of the current study was to develop an accountability system that was cost‐effective and could be used by a school administrator in the context of their regular school oversight duties. Along these lines, a central question posed by the school principal concerned the cost–benefit ratio of the procedure. While direct measures of cost‐effectiveness were not measured, it was estimated that the time to evaluate each of the student's summarized data and meet with the classroom was approximately 30 min per week, and was an efficient use of time for the result of increasing frequency of program implementation for these three students. Given the increase in frequency gained and the low time cost of monitoring and supporting the staff, the principal suggested the need to intervene with the remaining 10 classrooms in the school. Therefore, the need to evaluate the effects of fading the intervention in the maintenance condition was necessary for practical reasons.</p> <hd id="AN0150131653-21">Implications for practice</hd> <p>The results of the study provide the following implications for practice to improve the frequency of implementation of skill‐building objectives:</p> <p></p> <ulist> <item> Collect summarized performance data to monitor the frequency of program implementation by direct classroom staff.</item> <p></p> <item> Identify an expert change agent to:</item> <p></p> <item> o provide leadership</item> <p></p> <item> o support overall processes</item> <p></p> <item> o keep staff organized and focused on clinical outcomes</item> <p></p> <item> o identify and address obstacles to intervention.</item> <p></p> <item> Use research‐based implementation support strategies for the entire classroom teams, including:</item> <p></p> <item> o nondirective collaborative goal setting</item> <p></p> <item> o nondirective consultation</item> <p></p> <item> o feedback and reinforcement with the entire classroom staff.</item> <p></p> <item> Use continuous monitoring of summarized performance data to:</item> <p></p> <item> o objectively pinpoint problem areas</item> <p></p> <item> o evaluate support interventions</item> <p></p> <item> o make data‐based decisions to adjust the frequency of skill‐building program implementation.</item> <p></p> <item> Use direct observation of staff through clinical reviews to augment indirect measures of frequency and to verify delivery of student's current educational programs, progress, and the extent to which the programs are delivered correctly.</item> </ulist> <p>In summary, this study extends the implementation of science research by providing a cost‐effective accountability system for administrators to improve the frequency of implementation of skill‐building objectives. Given that the improved program implementation frequency maintained after the administrative oversight was lessened in the current study, this suggests that including direct care staff in collaborative system change efforts would be worthy of future research endeavors.</p> <hd id="AN0150131653-22">ACKNOWLEDGMENTS</hd> <p>The authors would like to thank the instructional team in Classroom 4 for their enthusiastic participation in all phases of this project. Special thanks are extended to Catherine Klebart, Denise Emma, and Tom Parvenski for their support of this investigation, and to the Capitol Region Education Council for their support of the Educational Leadership, Policy & Instructional Technology Cohort at Central Connecticut State University.</p> <ref id="AN0150131653-23"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref2" type="bt">1</bibl> <bibtext> Bailey, J. B., Burch, & M. R. (2002). Research methods in applied behavior analysis. 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European Journal of Behavior Analysis, 20 (1), 92 – 109. https://doi.org/10.1080/15021149.2019.1629781</bibtext> </blist> <blist> <bibtext> Fixsen, D. L., Naoom, S. F., Blase, K. A., Friedman, R. M., & Wallace, F. (2005). Implementation research: A synthesis of the literature. University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Research Network (FMHI Publication #231).</bibtext> </blist> <blist> <bibtext> Fovel, T. (2013). The new ABA program companion. DRL Books.</bibtext> </blist> <blist> <bibtext> Gersten, R., Compton, D., Connor, C. M., Dimini, J., Santoro, L., Linan‐Thompson, S., & Tilly, W. D. (2008). Assisting students struggling with reading: Response to intervention and multi‐tier intervention for reading in primary grades. A practice guide (NCEE 2009‐4045). National Center for Education Evaluation and Regional Assistance. Institute of Education Science, U.S. Department of Education.</bibtext> </blist> <blist> <bibtext> Greenway, R., McCollow, M., Hudson, R., Peck, C., & Davis, C. (2013). Autonomy and accountability: Teacher perspectives on evidence‐based practice and decision‐making for students with intellectual and developmental disabilities. Education and Training in Autism and Developmental Disabilities, 48 (4), 456 – 468. <ulink href="http://www.jstor.org/stable/24232503">http://www.jstor.org/stable/24232503</ulink></bibtext> </blist> <blist> <bibtext> Howard, J. S., Sparkman, C. R., Cohen, H. G., Green, G., & Stanislaw, H. (2005). A comparison of intensive behavior analytic and eclectic treatments for young children with autism. Research in Developmental Disabilities, 26, 359 – 383.</bibtext> </blist> <blist> <bibtext> Kucharczyk, S., Shaw, E., Smith Myles, B., Sullivan, L., Szidon, K., & Tuchman‐Ginsberg, L. (2012). 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Division of Behavioral and Social Sciences and Education. National Academy Press.</bibtext> </blist> <blist> <bibtext> Odom, S. L., Hall, L. J., & Suhrheinrich, J. (2019). Implementation science, behavior analysis, and supporting evidence‐based practices for individuals with autism. European Journal of Behavior Analysis, 21 (1), 55 – 73. https://doi.org/10.1080/15021149.2019.1641952</bibtext> </blist> <blist> <bibtext> Parsons, M. B., Rollyson, J. H., & Reid, D. H. (2012). Evidence‐based staff training: A guide for practitioners. Behavior Analysis in Practice, 5 (2), 2 – 11.</bibtext> </blist> <blist> <bibtext> Peck, C. A., Killen, C. C., & Baumgart, D. (1989). Increasing implementation of special education instruction in mainstream preschools: Direct and generalized effects of nondirective consultation. Journal of Applied Behavior Analysis, 22 (2), 197 – 210.</bibtext> </blist> <blist> <bibtext> Ruble, L. A., Dalrymple, N. J., & McGrew, J. H. (2010). The effects of consultation on individualized education program outcomes for young children with autism: The collaborative model for promoting competence and success. Journal of Early Intervention, 32 (4), 286 – 301. https://doi.org/10.1177/1053815110382973</bibtext> </blist> <blist> <bibtext> Sanetti, L. M. H., & Collier‐Meek, M. A. (2015). Data‐driven delivery of implementation supports in a multitiered framework: A pilot study. Psychology in the Schools, 52 (8), 815 – 828.</bibtext> </blist> <blist> <bibtext> Strain, P. S., & Hoyson, M. (2000). On the need for longitudinal, intensive social skill intervention: LEAP follow‐up outcomes for children as a case in point. Topics in Early Childhood Special Education, 20, 116 – 122.</bibtext> </blist> <blist> <bibtext> Sulzer‐Azaroff, B., Dyer, K., Dupont, S., & Soucy, D. (2012). Applying behavior analysis across the autism spectrum: A field guide for new practitioners (2nd ed.). Sloan Publishing.</bibtext> </blist> <blist> <bibtext> Sundberg, M. L. (2008). VB‐MAPP—Verbal behavior milestones assessment and placement program: A language and social skills assessment program for children with autism or other developmental disabilities guide. AVB Press.</bibtext> </blist> <blist> <bibtext> Thomson, K., Martin, G., Arnal, L. L., Fazzio, D., & C.T. Yu, D. T. (2009). Instructing individuals to deliver discrete‐trials teaching to children with autism spectrum disorders: A review. Research in Autism Spectrum Disorders, 3 (3), 590 – 606.</bibtext> </blist> <blist> <bibtext> Vollmer, T. R., Sloman, K. N., & St. Peter Pipkin, C. (2008). Practical implications of data reliability and treatment integrity monitoring. Behavior Analysis in Practice, 1 (2), 4 – 11. https://doi.org/10.1007/BF03391722</bibtext> </blist> <blist> <bibtext> Wilkerson, L. A. (2007). Assessing treatment integrity in behavioral consultation. International Journal of Behavioral Consultation and Therapy, 3 (3), 420 – 432.</bibtext> </blist> <blist> <bibtext> Wong, C., Odom, S. L., Hume, K., Cox, A. W., Fettig, A., Kucharczyk, S., Brock, M. E., Plavnik, Fleury, V. P., & Schultz, T. R. (2015). Evidence‐based practices for children, youth, and young adults with Autism Spectrum Disorder: A comprehensive review. Journal of Autism Spectrum Disorders, 45 (7), 1951 – 1966.</bibtext> </blist> <blist> <bibtext> Wood, A. L., Luiselli, J. K., & Harchik (2007). Training instructional skills with paraprofessional service providers at a community‐based habilitation setting. Behavior Modification, 31 (6), 847 – 855.</bibtext> </blist> </ref> <aug> <p>By Kathleen Dyer and Caroline Redpath</p> <p>Reported by Author; Author</p> </aug>
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  Data: Providing Implementation Supports to Intensify Instruction in an Autism Classroom
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  Data: <searchLink fieldCode="AR" term="%22Dyer%2C+Kathleen%22">Dyer, Kathleen</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-8424-3242">0000-0001-8424-3242</externalLink>)<br /><searchLink fieldCode="AR" term="%22Redpath%2C+Caroline%22">Redpath, Caroline</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Psychology+in+the+Schools%22"><i>Psychology in the Schools</i></searchLink>. Jun 2021 58(6):1041-1055.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: <searchLink fieldCode="DE" term="%22Program+Implementation%22">Program Implementation</searchLink><br /><searchLink fieldCode="DE" term="%22Autism%22">Autism</searchLink><br /><searchLink fieldCode="DE" term="%22Pervasive+Developmental+Disorders%22">Pervasive Developmental Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Skill+Development%22">Skill Development</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Skills%22">Language Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+Skills%22">Speech Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Goal+Orientation%22">Goal Orientation</searchLink><br /><searchLink fieldCode="DE" term="%22Consultation+Programs%22">Consultation Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement%22">Reinforcement</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink>
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  Data: 10.1002/pits.22486
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  Data: 0033-3085
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  Data: This study evaluated whether an implementation support package using collaborative goal setting, nondirective consultation, feedback, and reinforcement would result in an increase in the implementation of planned speech and language programs in a classroom for students with autism spectrum disorders. Additional measures were collected to assess generalization to other skill-building programs in the students' schedules that were not targeted in the intervention. The results of a multiple-baseline analysis revealed that the implementation supports were successful in increasing the frequency of implementation of speech and language programs to goal levels. Further, these gains were maintained when staff support was faded to a weekly schedule for two of the students. The third student required weekly staff support to maintain goal levels. Discontinuation of direct staff support in the maintenance phase resulted in higher and more consistent implementation frequency than seen in the baseline. Similar trends in the generalization targets were evidenced. The results are discussed in relation to implementation science for students with autism.
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        Value: 10.1002/pits.22486
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        StartPage: 1041
    Subjects:
      – SubjectFull: Program Implementation
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      – SubjectFull: Autism
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      – SubjectFull: Pervasive Developmental Disorders
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      – SubjectFull: Students with Disabilities
        Type: general
      – SubjectFull: Skill Development
        Type: general
      – SubjectFull: Language Skills
        Type: general
      – SubjectFull: Speech Skills
        Type: general
      – SubjectFull: Goal Orientation
        Type: general
      – SubjectFull: Consultation Programs
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Reinforcement
        Type: general
      – SubjectFull: Instructional Effectiveness
        Type: general
    Titles:
      – TitleFull: Providing Implementation Supports to Intensify Instruction in an Autism Classroom
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Dyer, Kathleen
      – PersonEntity:
          Name:
            NameFull: Redpath, Caroline
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 0033-3085
          Numbering:
            – Type: volume
              Value: 58
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Psychology in the Schools
              Type: main
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