An Evaluation of Assistive Technology in Determining Job-Specific Preference for Adults With Autism and Intellectual Disabilities

Abstract

The transition to employment can be difficult for adults with autism spectrum disorders (ASDs) and intellectual disabilities (IDs). Currently, a limited number of ASD-specific career-planning tools exist within the literature, creating a challenge in terms of accurately identifying jobs that match individual preferences and strengths. This study evaluated the effects of a technology-based prework assessment on job performance among 3 adults with ASD and ID, aged 20–21 years prior to beginning supported employment. Three job conditions were established: a high-preference, high-skill-match job; a high-preference, low-skill-match job; and a low-preference, low-skill-match job. The 3 job conditions were evaluated using an alternating-treatments design with supported-employment sessions counterbalanced across a 6-week period. The results indicated that the high-preference job conditions produced higher levels of job performance irrespective of skill match. Implications for future research and practice are discussed.

Autism spectrum disorder (ASD) impacts as many as 1 in 59 individuals, and its clinical expression is characterized by impaired social communication skills and the presentation of restricted, repetitive interests and behaviors (American Psychiatric Association, 2013; Baio et al., 2018). There has been a growing interest in outcomes for adults with ASD; however, there continues to be little research focused on employment (Walsh, Holloway, McCoy, & Lydon, 2016). The transition from educational services to employment is difficult for adults with ASD, and the majority of individuals with ASD are unemployed (Barnard, Harvey, Potter, & Prior, 2001; Billstedt, Gillberg, & Gillberg, 2005; Hendricks, 2010). For example, Taylor and Seltzer (2011) followed 66 adults with ASD after they exited the high school system and found that only 6% of adults with ASD were competitively employed, although not working full-time hours, and 12% were in supported employment.

To prepare people for employment, vocational training services engage individuals in supported employment as a means to acquire skills and experience. In line with person-centered planning, it is imperative that the individual’s preferences for a job are considered in the selection of supported employment (Hall, Morgan, & Salzberg, 2014; Morgan & Horrocks, 2011). However, limited research attention has been directed toward career-planning tools to assist individuals with ASD in the transition to the workplace (Murray, Hatfield, Falkmer, & Falkmer, 2016). Furthermore, the research on teaching specific job skills (e.g., using a photocopying machine) for adults with ASD and intellectual disabilities (IDs) has primarily focused on acquisition deficits rather than on performance deficits (Bereznak, Ayres, Mechling, & Alexander, 2012). Gresham, Van, and Cook (2006) propose that acquisition deficits relate to a person’s ability to learn, whereas the absence of knowledge impacts a person’s ability to perform a skill. Therefore, performance deficits can be described as knowing how to perform a skill but not using it (Gresham et al., 2006). For example, in the context of employment skills, an adult may have learned all the steps necessary to perform a task but not successfully emit the task or be motivated to perform the skills in work settings. Preference matching can potentially increase motivation for accuracy of task completion and impact performance in a positive way.

Preference assessments are commonly used in applied settings to identify preferences (Carr, Nicolson, & Higbee, 2000). Direct, trial-based methods of preference assessment can include single-stimulus (Hagopian, Rush, Lewin, & Long, 2001), paired-stimuli (Fisher et al., 1992), and multiple-stimuli (Ciccone, Graff, & Ahearn, 2005) presentations. One key aspect of many direct methods of preference assessment is choice. Providing choice can lead to increases in appropriate behavior, improved task engagement, and decreases in problem behaviors (Cannella, O’Reilly, & Lancioni, 2005; Romaniuk & Miltenberger, 2001). With regard to employment, several studies have shown that job performance is increased on high-preference jobs when compared to low-preference jobs for individuals with IDs (Bambara, Ager, & Koger, 1994; Morgan & Horrocks, 2011; Parsons, Reid, Reynolds, & Bumgarner, 1990). These findings highlight the clinical need for support staff and job coaches to assess and support individual preferences for employment types prior to the onset of work. However, although research evaluating the effects of preference assessments has been shown to be successful in determining work preferences for people with IDs working in supported employment (Parsons, Reid, & Green, 1998), limitations relating to feasibility and utility in the clinical setting have been noted. If a method for assessing preferences is time consuming and labor intensive, this can often reduce the likelihood that the procedure will be incorporated into routine practice (Carr et al., 2000; DeLeon et al., 2001). Thus, this poses the risk of service providers placing individuals with ASD and IDs in supported employment without any systematic assessment of their individual work preferences.

Research is needed to explore additional ways of assessing and identifying preferences. One alternative approach to identifying preferences is incorporating the use of assistive technology. The myPref app (KV Adaptive, 2016) was designed to assist with implementing a paired-choice preference assessment to determine an individual’s preference. In addition, video modeling is a technology-assisted intervention that is commonly incorporated into interventions for individuals with ASD and has been explored in the published literature (Alexander, Ayres, Smith, Shepley, & Mataras, 2013; Allen, Burke, Howard, Wallace, & Bowen, 2012; Goh & Bambara, 2013; Kellems & Morningstar, 2012). Video modeling allows for the presentation of an array of stimuli and depictions of the natural environment that may not be immediately assessable in the training setting (Ayres & Langone, 2005). Using video models to illustrate available supported-employment options could help build an individual’s capacity in making informed decisions about job preferences. Currently, however, the use of the myPref app and video modeling has not been investigated in determining the vocational preferences of adults with ASD and IDs.

Nevertheless, although effectively assessing and supporting an individual’s preference for employment is of utmost importance, job performance may not increase if a job requires skills that are not within a person’s repertoire (Hall et al., 2014). Hall et al. (2014) examined the effects of high-preference, high-skill-matched jobs (HPHMs) and low-preference, low-skill-matched jobs (LPLMs) among four adults, aged 19–20 years, with mild to moderate IDs. HPHMs were associated with higher levels of productivity and accuracy of tasks performed when compared to LPLMs among all participants. These results denote the importance of including a job-matching component and highlight some areas for future research, such as the inclusion of a third high-preference, low-skill-match condition (HPLM), which would provide valuable insight into what effects the degree of skill match had on job performance.

The benefits of choice have been widely documented within the disability literature, leading to improved task engagement and reduced problem behavior (Howell, Dounavi, & Storey, 2019). A prework assessment strategy to aid adult service providers to accurately identify jobs that match individual preferences is essential in order to support motivation and potentially decrease performance deficits. The aim of the current study is to compare the combined effects of the myPref app and the video models as a prework assessment in determining work preferences for adults with ASD and IDs. In addition, the current study seeks to extend the work of Hall et al. (2014) by addressing the two key limitations: (a) to include an HPLM and (b) to include six repetitions of each job condition.

Method

This study used an alternating-treatments design replicated across participants to evaluate the effects of the prework assessment on job performance. Participants were exposed weekly to three job conditions: an HPHM, an HPLM, and an LPLM. To minimize sequence effects, supported-employment sessions were counterbalanced. The six possible sequences were used an equal number of times across participants.

Participants

Adults with ASD and an ID were eligible for participation in the study and were recruited from a vocational rehabilitation training center for adults with ASD and ID. Three participants ranging in age from 20 to 21 years took part in the study. Pseudonyms were assigned to all participants to preserve anonymity and confidentiality. John was a 21-year-old Caucasian male with a diagnosis of ASD and a mild ID. Findings from his most recent psychological assessment indicated an IQ score of 60, as measured by the Stanford Binet, Fifth Edition (SB5; Roid, 2003). John verbally expressed his needs; however, he presented with difficulties initiating and maintaining conversations. He had the ability to follow directions and complete tasks accurately. Kathy was a 20-year-old Caucasian female with a diagnosis of ASD and a mild ID. Kathy had difficulty in processing auditory information, and just the nonverbal scale of the SB-5 was administered, showing a nonverbal IQ of 59. Kathy displayed high levels of stereotypic behavior (e.g., twirling, rocking) and had limited verbal communication. She had difficulty coping with busy environments and changes in her routine. Mark was a 20-year-old Caucasian male with a diagnosis of ASD, severe expressive and receptive language difficulties, attention deficit hyperactivity disorder, and a mild learning disability. Mark had difficulty concentrating on tasks, exhibited impulsive behaviors, and also had difficulties coping in busy environments. In social situations, a preference for staying on the periphery was reported. Participants’ scores pertaining to diagnoses and ID were obtained from their clinical records. None of the participants were employed at the time of the study. All participants were selected regardless of race, gender, or socioeconomic status. Participation in the study was voluntary, and informed written consent was obtained from each participant prior to the onset of the study. The research ethics committee at the university and the ethics committee from the disability service provider approved this study.

Settings

Supported-employment options were available in participants’ local community and in the vocational rehabilitation training center. The primary job sites available included a supermarket, a sportswear store, an office located in the headquarters of a disability service provider, a candy store, a training room, and a cafeteria located within the vocational rehabilitation training center. A job coach attended each supported-employment session with the participants. Customers, coworkers, and employers of the businesses and nonprofit organizations frequented the participants’ workspaces depending on the supported nature of the employment. The preassessment environment was a quiet classroom at the participants’ vocational rehabilitation training center. Individual participants and the first author met in the classroom in the vocational rehabilitation training center to conduct the preference assessment. The classroom consisted of a room with tables, chairs, an iPad, and a laptop computer.

Job Coach

Two students completing a master’s in applied behavior analysis, on clinical placement at the vocational rehabilitation training center, acted as job coaches. The job coaches were responsible for collecting data and attending the supported-employment placements with participants. The job coaches were blind to the supported-employment job conditions (i.e., HPHM, HPLM, and LPLM) and the specific details of the study.

Materials and Equipment

Video Models

Video models were used to depict the different supported-employment options available to the participants via their vocational rehabilitation training center. The video models were prepared using an adult model who demonstrated each job (e.g., an adult modeling stocking milk on shelves in a local supermarket). A total of 12 video models were created to illustrate the 12 supported-employment opportunities available. The video models were recorded using the video camera feature on an iPad Air Wi-Fi 32GB (running iOS 11.0.1) and contained no verbal descriptions. Each video model was between 58 s and 1 min 43 s in duration.

MyPref

The iPad application myPref (KV Adaptive, 2016) was used to assess participants’ job preferences. A still image from each video model of the adult model engaging in the job or items required for the job were uploaded onto the app prior to running the preference assessment. MyPref allows for six items to be compared during one preference assessment. Images were presented as a paired-choice preference assessment to determine a user’s preference. The myPref app runs 30 trials with each item pitted against every other option. For example, during an assessment each participant was repeatedly presented with images in an array of two and asked to select the preferred image. The number of times an image was selected was divided by the total number of choice pairs including that image and converted into a percentage. The myPref app calculated preference scores, and a higher percentage denoted a greater preference for that image (i.e., job). The myPref preference assessment method is similar to a standard paired-choice preference assessment in that it consists of the simultaneous presentation of two stimuli, and every possible pair of stimuli is presented. However, the stimuli are presented on-screen, and instead of an observer recording which of the two stimuli the individual chooses, the app calculates how many times each stimulus is chosen.

Dependent Measures

Job Performance

The job coach initially drew up a task analysis for each supported-employment option available. The employers were encouraged to identify changes in the task analyses through consultation with the job coach. However, no changes were identified. Following this, the task analyses were then confirmed after each step in the behavior chain was documented by the researcher and performed by an individual. The number of steps in each task analysis was similar across jobs, with an average of 82 steps per task analysis. Job performance was defined as the total number of correct independent responses divided by the total number of steps and multiplied by 100 to get a percentage. During the performance measure, the job coach instructed the participant to begin work (e.g., “Time to start work,” “Time to begin work.”). The job coach recorded the number of steps performed independently on the task analysis during a 15-min period. If the participant performed a step incorrectly or out of sequence or the response latency (6 s) had elapsed, the job coach used least-to-most prompting (i.e., independent, gestural, verbal, model, partial physical, full physical) to assist the participant in completing the step and positioned the participant for the next step. This was recorded as an incorrect response. If a participant refused to participate in a supported-employment session, the participant was further encouraged to take part by the job coach. However, if the participant declined to participate a second time, the job coach noted this and the supported-employment session did not occur.

Choice/Satisfaction

As a comparison to the prework assessment, participants were also asked at the end of every week which job they enjoyed the most. The job coach presented the participant with three pictures (previously used in the preference assessment) of the participant’s HPHM, HPLM, and LPLM jobs that had been completed. Participants were asked to select the image they preferred the most, and their response was recorded. The positioning of the array of images was rotated when presented in subsequent weeks.

Social Validity

At the end of the study, the researcher interviewed the job coaches, who were blind to the job conditions, and asked which job they thought best suited the participants. This included a measure of social validity and an additional measure of job matching.

Procedure

Preference Assessment

Twelve supported-employment options were available to participants through their vocational rehabilitation training center. The supported-employment options were designed to assist individuals with ASD and ID learn new skills, integrate, and participate in the work environment. The supported-employment opportunities included community-based jobs (e.g., supermarket) and jobs available in the training center (e.g., cafeteria). The supported-employment options available included (a) cleaning dishes in a restaurant, (b) letter folding and sorting mail in the local hospital, (c) stocking milk on shelves in a supermarket, (d) sales assistant in a candy store located in the participants’ training center, (e) tidying and organizing display clothes in a sportswear store, (f) preparing pay slips for trainees in the participants’ training center, and (g) setting tables in a cafeteria. Clerical jobs located in the offices of local organizations included (h) labeling envelopes, (i) shredding paper, (j) filing, (k) completing volunteer packs, and (l) laminating.

The preference assessments were conducted individually. First, participants were shown the video models of the 12 job placements on a laptop computer. Second, preferences for the different supported-employment options were assessed using the myPref app (KV Adaptive, 2016) on an iPad. Two preference assessments, each containing six images (representing jobs), were undertaken with each participant in order to present the 12 job options. The myPref app (KV Adaptive, 2016) presented each participant with 30 trials per assessment, thus presenting each pairing of items twice, with each item in the pair counterbalanced across positions, to prevent any potential side biases. For example, during a preference assessment, the participant was simultaneously presented with two images of supported-employment options and asked to select the preferred job. The participant indicated his or her preference on the screen by touching or pointing to the picture on the iPad. Participants did not contact the job associated with a given choice during the preference assessment. The first author assisted with technology issues and answered any questions that participants had in order to complete the preference assessment. When job preferences were identified for each participant, they were each exposed to a skill-matching assessment.

Skill-Match Assessment

During the skill-match assessment, the researcher assessed participants’ performance on the three most preferred jobs and the three least preferred jobs. In this session, the participants were required to complete the job. To assess the participant’s mastery level of the task prior to the onset of the supported-employment sessions, the single-opportunity method was used to ascertain participants’ ability to perform each behavior in the task analysis in the correct sequence (Snell & Brown, 2006). The percentage of tasks completed was calculated by recording each step performed correctly and dividing the correct number of steps by the total number of steps on the task analysis and multiplying by 100. A higher score represented a better skill match. The skill-match assessment was carried out in the supported-employment settings.

Job-Matching Procedure

The researcher then matched and assigned each participant to three supported-employment job conditions. The three job conditions included (a) an HPHM, (b) an HPLM, and (c) an LPLM. An HPHM consisted of a job that the participant highly preferred and was one that their skills most closely matched those required for the job. An HPLM was a job that the participant indicated as highly preferred and was one that their skills were a low match with the job requirements. An LPLM represented the participant indicating a low preference for the task and was one that the participant’s skills were a low match with the job requirements. High-match jobs were those with the highest percentage of correct responses completed on the task analysis, and low-match jobs were those with the lowest percentage of correct responses completed on the task analysis. Based on the results from the preference assessment, highly preferred jobs were those in the participants’ top three and low-preferred jobs were those in their bottom three.

Supported-Employment Sessions

Participants then carried out one HPHM, one HPLM, and one LPLM job session per week according to a counterbalanced schedule. A job coach attended each job session with the participant and recorded data on job performance. Participants worked in each supported-employment placement for 15 min.

Interobserver Agreement

A second observer simultaneously and independently recorded reliability data on the number of steps performed correctly on the task analyses during the skill-match assessment and supported-employment sessions. An agreement was defined as scoring the same response during each measure. Interobserver agreement (IOA) was calculated by dividing the total number of agreements by the total number of agreements plus disagreements and multiplying by 100.

Results

Preference Assessments

Results from the preference assessment are shown in Fig. 1. Preference scores for the different supported-employment opportunities ranged from 0% to 90%. Data from the skill-match assessment are presented in Table 1. Skill-match assessment scores were not higher for all high-preference jobs when compared to the skill-match assessment scores for the low-preference jobs. Table 2 illustrates the assignment of tasks to preference conditions for each participant (i.e., HPHM, HPLM, and LPLM).

Fig. 1.

Outcome of preference assessment. Supported-employment placements were (a) cleaning dishes in a restaurant, (b) letter folding and sorting mail in the local hospital, (c) stocking milk on shelves in a supermarket, (d) sales assistant in a candy store located in the participants’ training center, (e) tidying and organizing display clothes in a sportswear store, (f) preparing pay slips for trainees in the participants’ training center, (g) setting tables in a cafeteria. Clerical jobs located in the offices of local organizations included (h) labeling envelopes, (i) shredding paper, (j) filing, (k) completing volunteer packs, or (l) laminating

Table 1.

Outcomes of Skill-Match Assessments

	Kathy			John			Mark
	Job	Preference indication	Skill match	Job	Preference indication	Skill match	Job	Preference indication	Skill match
High-preference jobs	(g)*	80%	25%	(e)*	70%	58%	(c)*	90%	70%
	(i)*	77%	87%	(j)*	70%	31%	(g)*	88%	18%
	(b)	60%	83%	(h)	63%	59%	(l)	80%	73%
Low-preference jobs	(l)	30%	41%	(f)	40%	35%	(b)	27%	57%
	(k)	30%	16%	(b)	30%	65%	(f)*	0%	0%
	(d)*	18%	0%	(g)*	20%	33%	(i)	0%	24%

Jobs were (b) letter folding and sorting mail in the local hospital, (c) stocking milk on shelves in a supermarket, (d) sales assistant in a candy store located in the participants’ training center, (e) tidying and organizing display clothes in a sportswear store, (f) preparing pay slips for trainees in the participants’ training center, and (g) setting tables in a cafeteria. Clerical jobs located in the offices of local organizations included (h) labeling envelopes, (i) shredding paper, (j) filing, (k) completing volunteer packs, or (l) laminating. An asterisk denotes job conditions (i.e., HPHM, HPLM, and LPLM)

Table 2.

Assignment of tasks to job conditions

Participants	Job conditions
	HPHM	HPLM	LPLM
Kathy	Clerical work (i.e., shredding paper)	Setting tables in cafeteria	Sales assistant
John	Organizing clothing in sportswear store	Clerical work (i.e., filing tasks)	Setting tables in cafeteria
Mark	Stocking milk in supermarket	Setting tables in cafeteria	Preparing wages in training center

Job Performance

Two participants successfully completed all 18 supported-employment sessions, and one participant completed 15 sessions. Fig. 2 illustrates the percentage of steps performed correctly for Kathy during each supported employment. During the HPHM condition, a high, stable level of responding was observed, with the percentage of steps performed correctly ranging from 89% to 97% (M = 92.93; SD = 3.25). During the HPLM condition, an initial increase in trend was observed, followed by a high, stable level for Sessions 2–6. The percentage of steps in the HPLM condition ranged from 36% to 98% (M = 83.54; SD = 23.72). During the LPLM condition, Kathy declined to participate for the first three sessions but later opted to participate in this condition. For LPLM, the data show an ascending trend, with steps performed correctly ranging from 5% to 54% (M = 24.33; SD = 26.45). However, the level of responding in LPLM did not reach that of HPHM or HPLM.

Fig. 2.

The percentage of steps performed correctly for Kathy during each job condition

Figure 3 depicts the results of the comparison of the HPHM, HPLM, and LPLM job conditions for John across the job performance outcome measure. During the HPHM condition, an initial increase in responding was observed, followed by a high, stable level of responding for Sessions 2–6. During HPHM, the percentage of steps performed correctly ranged from 61% to 99% (M = 91.19; SD = 14.78). A similar pattern was observed for the HPLM condition, with an initial increase in responding observed followed by a high, stable level of responding from Sessions 2–6. For HPLM, the percentage of steps performed correctly ranged from 43% to 100% (M = 89.17; SD = 22.75). During the LPLM condition, fewer steps performed correctly were recorded (range 37%–68%; M = 52.88; SD = 9.98), and a gradually increasing trend was observed. However, the level of responding in LPLM did not reach the high, stable rates obtained for HPHM and HPLM.

Fig. 3.

The percentage of steps performed correctly for John during each job condition

Job performance data for Mark are presented in Fig. 4. During the HPHM condition, high levels of responding were observed with the percentage of steps performed correctly ranging from 70% to 88% (M = 77.59; SD = 6.79). In the HPLM condition, an increasing trend in responding was observed, followed by high, variable levels of responding from Sessions 2–6. For HPLM, the percentage of steps ranged from 25% to 85% (M = 66.87; SD = 21.98). During the LPLM condition, an ascending trend was observed, ranging from 9% to 57% (M = 33.54; SD = 22.19). The level of responding in LPLM did not reach the high rates obtained for HPHM and HPLM.

Fig. 4.

The percentage of steps performed correctly for Mark during each job condition

Choice/Satisfaction

Participants were asked at the end of each week which job they enjoyed the most. Each participant chose the highly preferred jobs (HPHM or HPLM) on 100% of the choice options. Kathy chose the HPHM job 67% (n = 4) of the time during the choice options, the HPLM job 33% (n = 2) of the time, and the LPLM job 0% (n = 0) of the time. John chose the HPHM job 83% (n = 5) of the time, the HPLM job 17% (n = 1) of the time, and the LPLM job 0% (n = 0) of the time during the choice options. Similarly, Mark chose the HPHM job 83% (n = 5) of the time, the HPLM job 17% (n = 1) of the time, and the LPLM job 0% (n = 0) of the time during the choice options. Choice/satisfaction outcomes are presented in Fig. 5.

Fig. 5.

Choice/satisfaction outcomes for each participant

Social Validity

At the end of the study, the researcher asked the job coaches, who were blind to the job conditions, which job they thought best suited the participants. Each job coach chose a high-preference job. Kathy’s job coach felt she was best suited to the supported-employment placement in the cafeteria (HPLM), which involved preparing the cafeteria tables for lunch breaks. John’s job coach said that he thought the office work, which involved filing paperwork (HPLM), suited John the best. However, he also noted that he felt John enjoyed the work in the sportswear store more (HPHM). Mark’s job coach thought the supported-employment placement in the supermarket best suited him (HPHM).

Interobserver Agreement

IOA was conducted during 28% of the skill-match assessments, and mean IOA was 100%. IOA was calculated for 33% of Kathy’s supported-employment sessions (M = 98%; range 94%–100%), 33% of John’s supported-employment sessions (M = 96%; range 89%–100%), and 33% of Mark’s sessions (M = 97%; range 92%–100%). Total IOA across participants was 97% (range 96%–98%). Job coaches were blind to the preference and skill-match assessment outcomes associated with each job condition evaluated.

Discussion

The purpose of this study was to evaluate the combined effects of the myPref app (KV Adaptive, 2016) and the video models as a prework assessment in determining work preferences for adults with ASD and ID. In addition, this study sought to extend the work of Hall et al. (2014) with the addition of six repetitions of each job condition and the inclusion of an HPLM job condition to explore what effects the degree of skill match had on job performance. The participants sampled three different supported-employment placements over a period of 6 weeks. Each week, participants were exposed to three job conditions: (a) HPHM, (b) HPLM, and (c) LPLM. Individual work preferences were identified using the myPref app (KV Adaptive, 2016) on the iPad, and skill level was assessed using task analyses. Video models were used to illustrate supported-employment opportunities and to help build participants’ capacity in making informed decisions about job preferences.

Overall, the results suggest that the intervention was effective in assessing the work preferences of all three participants. When participants were provided with a choice and asked to choose which job they enjoyed the most at the end of every week, each participant chose the job that the prework preference assessment had indicated was highly preferred more frequently than the least preferred job. The results of this study also suggest that work preference and choice can have a potential impact on performance within the employment setting. All three participants performed higher (i.e., completed the elements of the task analysis) in the high-preference job conditions when compared to the low-preference job conditions. The findings are consistent with those of previous studies on job preference for adults with IDs (Morgan and Horrocks, 2011). Furthermore, when preference and skill level were matched, the highest job performance scores were recorded. These findings are also similar to previous research on the effects of job preference and skill match. For example, Hall et al. (2014) found that HPHM jobs were associated with higher levels of productivity and accuracy of work tasks performed when compared to LPLM jobs among participants with mild to moderate IDs.

Another important finding of note is that in the HPLM condition, all three participants showed ascending trends greater than in the LPLM condition, with rapid changes in job performance by Session 2. After Session 3, all participants performed comparably in the HPHM and HPLM job conditions. These results indicate that the high-preference job conditions (i.e., HPHM, HPLM) produced higher levels of job performance irrespective of skill match. The practical implications of the results suggest that if strong preferences for a particular job are apparent, then this can exceed skill match. The findings of this study show tentative evidence that if an individual expresses a high preference for a task but doesn’t have a high skill match, they can still perform very well as opposed to not being interested and not having the skills. It is important for service providers to be aware that individuals who may not have a high skill level for a particular job at the outset should not be prohibited from taking on that job. If a job is a high preference but a low skill match, participants may still achieve comparable performance. Future research should explore the effects of a low-preference, high-skill-match condition to further illustrate the role of choice and preference in skill acquisition. When comparing the HPLM and LPLM job conditions, all three participants showed increased learning (i.e., learned more steps on the task analysis) in both conditions but more rapidly in the HPHM condition. However, the preference for the LPLM condition never increased despite participants being able to perform more steps of the job. Participants never selected the LPLM job condition during the choice/satisfaction assessment, thus highlighting the importance of assessing preference and not just skill match when identifying the most suitable job for an individual (i.e., just because an individual can do a job doesn’t mean the person enjoys it).

It should be noted that job performance in the LPLM condition started to show an increasing trend for two of the participants. This highlights how skill level can change over time with exposure to supported-employment sessions. Although the researchers tried to minimize teaching during the supported-employment sessions, participants may have learned how to perform each job on the task analysis during subsequent opportunities. After Week 4, there was an increase in the LPLM condition for Kathy. As Kathy became more familiar with her role as a sales assistant, her job performance began to increase. Similarly, as Mark was repeatedly exposed to the accounts job, we also saw an increase in job performance. Increased performance in the LPLM job conditions provides us with some insight into an LPHM job condition, as the skill match improved over the course of the study. Nevertheless, the low-preference job condition (i.e., LPLM) was never selected when participants completed their weekly choice assessment, thus indicating that even if an individual can complete a skill, it does not mean they will like the job. Therefore, when matching individuals to supported employment, it is important to place a greater emphasis on preference because the findings show that skills can be taught but preference may not change (e.g., LPLM was never selected during the choice/satisfaction measure).

These results have implications for the use of this simple job-matching procedure as a method to increase job performance among adults with ASD and co-occurring ID. According to Murray et al. (2016), it is important to examine the clinical utility of a tool, whether or not the tool is practical and easy to implement. This study adds to the current literature base by addressing these shortcomings and analyzing the feasibility of the existing assessment procedure. The job-matching procedure was inexpensive, with one credit for the myPref app (KV Adaptive, 2016) costing $0.99. However, it should be noted that identifying preferences and skill match prior to the onset of supported employment is not the end point for adults, job coaches, and other stakeholders. Preferences and skill match should be frequently assessed and reassessed throughout the job-sampling procedure. Accurately assessing preference may improve job performance and task engagement, potentially leading to a more positive experience with employment.

This study builds on previous research by using the myPref app (KV Adaptive, 2016) together with video modeling to identify work preferences in adults with ASD and IDs. The findings support the use of myPref and video modeling in determining individuals’ work preferences prior to beginning supported employment. It is important that individuals entering vocational rehabilitation training programs have a positive experience with work, thus increasing the likelihood that they will be motivated to continue on the path toward employment. As a limited number of career-planning tools exist for adults with ASD (Murray et al., 2016), this prework assessment may provide an avenue for job coaches and special education personal to incorporate the use of assistive technology in supporting adults with ASD and ID transition into the workplace.

Overall, the results suggest that this combination of video modeling and the myPref app (KV Adaptive, 2016) offers promise as a tool to adequately identify work preferences prior to the onset of supported employment in order to enhance job performance among adults with ASD and IDs. However, a few limitations should be considered when interpreting the results. First, the prework assessment took place at the beginning of the study, with job preference and skill match only assessed prior to the beginning of supported employment. Preference is variable and transitory in nature. Preferences can change based on exposure, social interactions with others, time of day, and the presence of certain establishing operations (Gottschalk, Libby, & Graff, 2000). Future research should frequently reassess job preference and skill match to examine the effects exposure to supported employment can have on these variables. Second, the findings of this study depict the combined effects of video modeling and the app, not either in isolation, and a component analysis of the prework assessment would be of benefit for further research. Further research should explore if the video models are necessary or if the app alone could be used to assess preferences. Third, the single-opportunity method utilized in the skill-match assessment may be considered a limitation because a low-skill -match job may not have been deemed so if additional sessions had been conducted for each job during the skill-match assessment. For example, a job deemed a low skill match during the initial session may be considered a high skill match based on higher scores obtained during subsequent sessions. However, it should be noted that no instruction was delivered during the skill-match assessment. The single-opportunity method assesses an individual’s ability to perform each behavior in the task analysis in the correct sequence, and assessment is terminated when a step is emitted incorrectly. Although this method is relatively quick to conduct and reduces the likelihood that learning will take place during the assessment (Snell & Brown, 2006), it provides the clinician with less information on later steps in the task analysis that may already be in an individual’s repertoire. However, the results showed a large separation in levels of performance between the HPLM jobs compared to the LPLM jobs across all participants, thus validating the use of a single session for each job to assess skill match. Fourth, participants did not contact the job associated with a given choice. Therefore, it could be argued that the participants selected a picture they preferred rather than a job they would prefer. The researchers tried to control for this bias through the use of video models to depict available jobs and help participants make informed decisions about their job preferences. Nonetheless, this limitation could be addressed in future research by carrying out a paired-choice preference assessment followed by exposure to the job for a brief period. Future research should also explore an alternative method of assessing preference that would allow participants to choose which job they engaged in for several days. This would allow for a clear demonstration of a preferred job. Fifth, IOA was collected during the skill-match assessment and during the supported-employment sessions. IOA or treatment integrity data were not collected for the preference assessment. The use of technology (i.e., myPref app) likely reduces the risk of procedural errors due to the decreased opportunity for human error. However, procedural errors may still occur when using technology, and it is still important to measure treatment integrity to understand the degree to which procedures were implemented as prescribed. A further limitation is the lack of variety of job tasks and supported-employment opportunities available. Similar to other research, the job tasks performed in this study may not be representative of the range of tasks actually performed in competitive employment environments (Hall et al., 2014). However, for the purpose of this study, a good range of opportunities was available to test the research question.

Findings from the current study support the effectiveness of this prework assessment on determining work preferences and predicting job performance among adults with ASD and ID prior to beginning supported employment. In summary, the procedure for identifying HPHM jobs was successful in identifying the most preferred job for each participant, and this is reflected in the fact that each participant ranked that job (i.e., HPHM) as the most preferred most frequently when asked each week across 6 consecutive weeks. This prework assessment process may provide a useful alternative for adult service providers and employment training programs to adequately identify preferences and skills match where traditional methods for evaluation of vocational preferences have been unsuccessful.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.