Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Something for everybody? Assessing the suitability of AAC systems for children using stated preference methods

Something for everybody? Assessing the suitability of AAC systems for children using stated... AUGMENTATIVE AND ALTERNATIVE COMMUNICATION https://doi.org/10.1080/07434618.2023.2206582 RESEARCH ARTICLE Something for everybody? Assessing the suitability of AAC systems for children using stated preference methods a,b a,b c d d Edward J. D. Webb , David Meads , Yvonne Lynch , Nicola Randall , Simon Judge , e e e b,f e Juliet Goldbart , Stuart Meredith , Liz Moulam , Stephane Hess and Janice Murray a b c Leeds Institute of Health Sciences, University of Leeds, Leeds, UK; Choice Modelling Centre, University of Leeds, Leeds, UK; School of Linguistic, Speech and Communication Sciences, Trinity College Dublin, Dublin, Ireland; Assistive Technology Team, Barnsley Hospital NHS e f Foundation Trust, Barnsley, UK; Faculty of Health and Education, Manchester Metropolitan University, Manchester, UK; Institute for Transport Studies, University of Leeds, Leeds, UK ABSTRACT ARTICLE HISTORY Received 21 January 2022 Little is known about what features of AAC systems are regarded by AAC professionals as more suit- Revised 2 September 2022 able for children with different characteristics. A survey was conducted in which participants rated the Accepted 13 December 2022 suitability of hypothetical AAC systems on a Likert scale from 1 (very unsuitable)to 7 (very suitable) alongside a discrete choice experiment. The survey was administered online to 155 AAC professionals KEYWORDS in the United Kingdom of Great Britain and Northern Ireland. Statistical modeling was used to estimate Children; clinical decision- how suitable 274 hypothetical AAC systems were for each of 36 child vignettes. The proportion of making; discrete choice AAC systems rated at least 5 out of 7 for suitability varied from 51.1% to 98.5% for different child experiment; likert scale; vignettes. Only 12 out of 36 child vignettes had any AAC systems rated at least 6 out of 7 for suitabil- stated preferences ity. The features of the most suitable AAC system depended on the characteristics of the child vignette. The results show that, while every child vignette had several systems that had a good suit- ability rating, there were variations, that could potentially lead to inequalities in provision. Augmentative and alternative communication (AAC) can representation would better suit the child’s future needs improve the lives of many people with communication diffi- given their progress. For more details, see Beukelman and culties (Hajjar et al., 2016; Ryan et al., 2015; Schlosser & Light (2020). Wendt, 2008). For children, provision of AAC is especially Research has revealed some important factors in AAC pro- important, as it can affect their social participation as well as fessionals’ decision-making such as a child’s preferences and their development and learning, having an impact on the family circumstances (Enderby et al., 2013; Geytenbeek et al., rest of their lives (Lund & Light, 2006; Ryan et al., 2015). In 2015; Thistle & Wilkinson, 2015). Assessing the cognitive demands of a given AAC system places on a child is another recent years, the expectations of people who use AAC to participate in all aspects of society has increased (Hemsley & important factor (Fried-Oken et al., 2019). There are also guidelines for how AAC services should be organized (Choi & Murray, 2015; Hynan et al. 2015; Light, McNaughton et al. 2019; Sundqvist & Ronnberg, 2010; Williams et al., 2008, Pak, 2006; National Health Service (NHS) England, 2016; 2012). Royal College of Speech and Language Therapists, 2009). Many different AAC systems exist, with very different fea- Some research has been conducted on AAC professionals’ tures. Children may benefit from AAC due to a wide range of decision-making. For example, it has been shown that pro- reasons and may have a variety of conditions such as cere- fessionals with different backgrounds (Dietz et al., 2012) and bral palsy and autism spectrum condition. Children with the levels of experience (Sauerwein & Wegner, 2020) differ in same diagnosis each have disparate needs, abilities, and per- their consideration of factors. Still, little is known about the sonal circumstances. Selecting a suitable AAC system for a details of how AAC professionals make decisions, or what child is thus a highly complex task, requiring the balancing features lead AAC professionals to judge AAC systems as a of many different competing concerns, and the process is suitable match for children with different characteristics unique to each child (Dietz et al., 2012; Lund et al., 2017; (Dietz et al., 2012; McFadd & Wilkinson, 2010; Ryan et al., Lynch et al., 2019). For example, matching might involve 2015). assessing whether a child is more motivated by vocabulary The current study examined AAC professionals’ judgment represented by photos or text. This consideration would and decision-making for children, specifically how suitable have to be balanced against which mode of graphical they believe different AAC systems are for different children. CONTACT Edward J. D. Webb e.j.d.webb@leeds.ac.uk Leeds Institute of Health Sciences, University of Leeds, Worsley Building, Clarendon Way, Leeds, LS2 9NL, UK Supplemental data for this article can be accessed online at https://doi.org/10.1080/07434618.2023.2206582 2023 International Society for Augmentative and Alternative Communication 2 E. J. D. WEBB ET AL. Figure 1. Schematic representation of the relationship between the current study and the wider I-ASC project. (By AAC system we refer in this manuscript to aided systems different AAC systems to be for a child, and how do their which may be either high-tech or low-tech.) Examining AAC judgements change depending on child characteristics? This professionals’ judgment and decision-making addresses the study adds information about strength of preference, reveal- previously noted knowledge gap and allows an examination ing not only what AAC system an AAC professional would of whether a gap exists between research and practice. The choose for a child, but also how suitable they believe the study also makes it possible to reflect on how improvements system to be for that child. The context for the current study is the UK, where it is may be made to current practice and service structures. The current study is part of three linked studies that used estimated that 1 in 200 children could benefit from AAC different methods to address the topic of AAC professionals’ (Enderby et al., 2013; Gross, 2010; Judge et al., 2017). There judgment and decision-making from different angles. The is some variation across the UK in how children are allocated principal research question for the first study (Webb et al. AAC systems, but in general children’s needs, abilities and 2019b) was: What is the relative importance of AAC system circumstances are assessed by a multidisciplinary team of attributes and child characteristics in AAC professionals’ deci- AAC professionals. Final recommendations are made with sion-making in daily practice? The study revealed how input from children and their support network about their important different factors were in AAC professionals’ daily preferences and opinions about the potential options. The practice; in other words how important specific factors were composition of the multidisciplinary team varies, and can averaged over the case-mix AAC professionals see. For include speech and language therapists, occupational thera- example, it revealed that interface-related AAC system attrib- pists, and teachers (Lynch et al., 2019; NHS England, 2016). utes were relatively more important than hardware attributes The current study was part of a wider research project in professionals’ daily practice. Averaged over the case-mix entitled Identifying Appropriate Symbol Communication aids that they saw, participants also judged children’s cognitive for children who are non-speaking: enhancing clinical deci- and learning abilities to be more important than their phys- sion-making (I-ASC) (Murray et al., 2020). The project used a ical features. The second study (Webb et al., 2019a) had the variety of research methods (Judge et al., 2020; Lynch et al., principal research question: When choosing an AAC system, 2019; Murray et al., 2019) to study AAC provision for children what tradeoffs do AAC professionals make between system in the UK. The evidence produced by the project has been attributes, and how do such tradeoffs change depending on used in the creation of a suite of resources for AAC profes- the characteristics of the child? This study revealed how AAC sionals and other stakeholders to support best practice, professionals make decisions when choosing for an individ- which is available for free at https://iasc.mmu.ac.uk/. See ual child. For example, it showed that children’s motivation Figure 1 for a schematic overview of the relations between to communicate using AAC and predicted future abilities the current study and the different components of the I-ASC had a greater influence on how participants traded-off differ- project. ent AAC system attributes than their language ability and The current study contributed to the wider project by showing how suitable AAC professionals judged different previous experience with AAC. Finally, the current study has the principal research ques- AAC systems to be for children with different characteristics. tion: How do AAC professionals judge the suitability of It examined whether, in the opinion of survey participants, AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 3 there was an AAC system which suited the needs of every the I-ASC project’s website and social media. The survey was individual child. In addition, it complemented investigations open for responses from 20 October 2017 until 4 March 2018. as to what systems AAC professionals would choose, as they A total of 172 people submitted complete responses, of which 155 completed the DCE and Likert scale tasks. may not necessarily choose what they believe to be the Participant demographics are summarized in Table 2. The most suitable AAC system for a child. For example, the most demographics of participants who did not complete the DCE suitable AAC system may not be chosen due to resource or Likert scale tasks were largely similar to those who did; constraints such as cost or instruction time. however, they were slightly older on average, at 46 years, and were more likely to have a professional background as Method an occupational therapist (n¼ 5, 29.4% compared to n¼ 11, 7%) than a background in speech and language therapy This study used data collected from the same participants as (n¼ 8, 47.1% compared to n¼ 117, 75.5%). Webb et al. (2019a), as well as the same procedures. Materials and measures Research design The study used a discrete choice experiment survey with The study used a survey design that combined a discrete additional Likert scale questions. The survey development choice experiment (DCE) and Likert scale ratings. The current and administration are described in the section that follows. study analyzed data from the Likert scale tasks which were asked after each DCE task in which participants rated how good a match an AAC system was for a child vignette on a Procedures scale from 1 (very unsuitable)to 7 (very suitable). The current In Webb et al. (2019b), lists of 18 AAC system attributes and study is concerned with cardinal measures of an AAC sys- 19 child characteristics were constructed using information tem’s suitability for a child. from systematic reviews of the literature (Judge et al., 2020), Likert scale ratings allowed the research question of how input from an expert panel as well as focus groups and inter- suitable a match different AAC systems are for different chil- views with AAC professionals, people who use AAC, their dren to be addressed. The innovative method of combining families and other stakeholders (Lynch et al., 2019; Murray a Likert scale with a DCE enabled the estimation of suitability et al., 2019). These lists were then included in a survey that ratings for many more AAC system-child combinations than used a method called best-worst scaling Case 1 (BWS) would be feasible for survey participants to rate individually. (Cheung et al., 2016) and that was administered to 93 AAC The study received ethical approval from an NHS professionals. Research Ethics Committee (REC reference 6/NW/0165). From the BWS attributes, five AAC system attributes and Participants gave informed consent. four child attributes were selected for inclusion in a DCE/Likert scale survey based on the following criteria: (a) attributes formed coherent descriptions of AAC systems/chil- Participants dren, (b) attributes reflected the specific aims of the I-ASC The target population was AAC professionals working in the project, (c) most attributes were of high relative importance UK who contributed to AAC decision-making for children. according to the BWS survey results, and (d) the number of Participants were recruited using the email lists of the I-ASC attributes was not so large as to overburden DCE survey project and Communication Matters (a UK-wide AAC charity respondents. The final list of attributes and levels for the and chapter of the International Society for Augmentative DCE is given in Table 1. and Alternative Communication), as well as project members’ The statistical design of the DCE was constructed using personal contacts. In addition, the survey was advertised on Ngene (ChoiceMetrics). This software package selected a Table 1. Attributes and levels for discrete choice experiment and likert scale survey. Attribute Levels Children Receptive and expressive language Delayed/ Receptive language exceeding expressive language Communication ability with AAC No previous AAC experience/ Able to use AAC for a few communicative functions/ Able to use AAC for a range of communicative functions Child’s determination and persistence Does not appear motivated to communicate through any methods and means/ Motivated to communicate through symbol communication systems/ Only motivated to communicate through methods other than symbol communication Predicted future skills and abilities Regression/ Plateau/ Progression AAC systems Vocabulary sets No vocabulary set / Fixed vocabulary set/ Vocabulary set with staged progression/ Size of vocabulary Up to 50 vocabulary items /50–1000 vocabulary items/ More than 1000 vocabulary items Consistency of layout Consistency of some aspects of layout / Consistency of all aspects of layout/ Idiosyncratic layout Type of vocabulary organization Visual scene / Taxonomic/ Semantic-syntactic/ Pragmatic Graphic representation Photos / Pictographic symbol set/ Ideographic symbol system (with rules or encoding)/ Text Indicates baseline level. 4 E. J. D. WEBB ET AL. Table 2. Participant demographics (N¼ 172). answer only demographic questions and were not shown Completed DCE Did not complete DCE the DCE or Likert scale tasks. The survey was administered online by a market research Characteristic (n¼ 155) % (n¼ 17) % company. Before starting the survey, participants were given Age 40.2 10.9 46.1 10.9 Years of experience 11.4 9.15 11.6 9.87 instructions, including attributes/characteristics and levels Female 140 90.3 15 88.2 descriptions. In each DCE task, participants were shown a White ethnicity 137 88.4 12 70.6 child vignette formed from the set of child attributes. For Professional background Speech and language therapist 117 75.5 8 47.1 example: Occupational therapist 11 7.1 5 29.4 Assistive technology specialist 5 3.23 0 0 Child A has receptive language exceeding expressive language. Teacher 11 7.1 3 17.6 Child A is able to use AAC for a few communicative functions. Other 12 7.74 0 0 Child A is motivated to communicate through symbol Common diagnoses communication systems. Child A is predicted to plateau in skills Autism spectrum 101 65.2 12 70.6 and abilities. Physical 128 82.6 12 70.6 Dyspraxia 12 7.74 2 11.8 In each task, participants were shown three hypothetical Intellectual disability/delay 107 69 11 64.7 Neurological 39 25.2 6 35.3 AAC systems described in term of the attributes in Table 1 Speech/language disorder 19 12.3 3 17.6 and asked which they would choose for the child vignette. Syndromes 56 36.1 5 29.4 An example choice task, including example AAC systems, is Location North West England 20 12.9 2 11.8 shown in Figure 2. After making their choice, participants North East England 5 3.23 1 5.88 were asked to rate how suitable their chosen AAC system Yorkshire and Humber 22 14.2 1 5.88 was for the child vignette using a Likert scale that ranged West Midlands 12 7.74 1 5.88 East Midlands 11 7.1 1 5.88 from 1 (very unsuitable)to 7 (very suitable). Participants were East of England 14 9.03 3 17.6 shown three randomly chosen child vignettes. For each child South West England 8 5.16 0 0 East England 32 20.6 3 17.6 vignette they completed four DCE and Likert scale tasks, London 18 11.6 4 23.5 meaning a total of 12 DCE and Likert scale tasks. An example Northern Ireland 5 3.23 0 0 survey can be found in the supplementary material of Webb North Wales 3 1.94 0 0 Wales 5 3.23 0 0 et al. (2019a). Further details about survey development are Mid-Wales 3 1.94 0 0 reported in Webb et al. (2021) and Webb et al. (2019a). Southern Scotland 7 4.52 0 0 Central Scotland 11 7.1 1 5.88 Northern Scotland 6 3.87 0 0 Non-UK 4 2.58 0 0 Statistical Analysis DCE: discrete choice experiment. Analysis of responses used a random utility theory frame- Mean and standard deviation. work (Louviere et al., 2000) that assumed individuals assigned a utility to each option. The utility of each option design that maximized D-efficiency, which may be thought was modeled as depending partly on the attributes of AAC of as a measure of how much information it is possible to systems as well as having a random component, represent- extract from survey responses (Kuhfeld et al., 1994). The ing all aspects of decision-making not explicitly captured by design had 60 tasks which were divided into five blocks of the model. Individuals were then assumed to choose the 12. Each participant was randomly allocated to answer a AAC system with the highest utility, and rated AAC systems block of 12 questions, with random allocations of blocks and higher if they had a higher utility. child vignettes independent of each other. It was possible to Ratings and choices were analyzed jointly using choice- form 54 child vignettes and 432 AAC systems from the sets ordered logit models (Webb & Hess, 2021) that had a set of of attributes. A total of 18 child vignettes and 158 AAC sys- parameters representing how individuals made their deci- tems were identified as representing unrealistic combinations sions. Statistical techniques were used to find the parameters and excluded from being used in the survey according to that maximized the probability of observing the choice and the judgements of authors with AAC expertise (an example ratings participants made. The full model with parameters is that it would be unrealistic to have a vocabulary set with for every interaction between AAC system and child attrib- staged progression with fewer than 50 vocabulary items). utes had too many parameters to estimate robustly. The survey was piloted with five AAC professionals. In Therefore, an iterative process was used in which a series of response to feedback, small changes were made to wording models with only one parameter were estimated. The param- and visual presentation to improve clarity. Piloting revealed eter that contributed most to explaining how participants that some AAC professionals did not have enough input into made their decision was selected for inclusion. A further ser- decision-making in their daily practice to meaningfully ies of models with two parameters were then estimated, and engage with the DCE/Likert scale tasks. To address this, at again the parameter that contributed most to explaining par- the beginning of the survey, participants answered the ques- ticipants’ decision-making was selected. This continued until tion “I confirm my work involves assessing children for aided all parameters were included. The final model was then AAC systems and I contribute to the decision-making in rela- selected using the Akaike information criterion (Akaike, tion to the language and vocabulary organization within AAC systems.” Those who responded no were directed to 1974), a measure of how well a model fits a dataset (see AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 5 Figure 2. Screenshot of example discrete choice experiment task. Figure 1, Supplemental file, for technical details of the model Of the 24 child vignettes without an AAC system, 11 rated estimation). 6 or higher were predicted to regress in skills and abilities, The final model was used to predict participants’ ratings whereas eight were predicted to plateau and four were pre- for every AAC system for every child vignette. It was then dicted to progress. In contrast, out of the 12 vignettes with an AAC system rated at least 6, seven were predicted to pro- calculated for each child vignette what percentage of AAC systems had a rating of at least 5 out of 7, and what per- gress in skills and abilities, four to plateau and one was pre- dicted to regress. All but one of the 12 child vignettes with centage had a rating of at least 6 out of 7. All model estima- at least one AAC system rated 6 or above for suitability were tion was carried out using the Apollo choice modeling motivated to communicate using AAC. package for R (Hess & Palma, 2019). Figure 3 shows how the most suitable AAC systems for each child vignette were rated. The vignette “delayed recep- tive and expressive language, no AAC experience, not moti- Results vated to communicate by any means, expected to regress in The raw results for model estimation are given in Table 1, skills and abilities” had the lowest rated most suitable AAC Supplemental file. Table 3 gives for each child vignette the system, at 5.62. The vignette “receptive language exceeding percentage of all 274 AAC systems included in the survey expressive language, experience of using AAC for a range of that were rated above 5 and above 6. All child vignettes had functions, motivated to communicate using AAC, expected at least 51.1% of AAC systems rated above 5, and for 19 out to progress in skills and abilities” had the highest rated most of 36 this percentage was above 90%. For 24 out of 36 child suitable system, at 6.62. The difference of 1 between the rat- vignettes, no AAC system was rated at 6 or above. However, ings of the most suitable AAC system represents 14.3% of some child vignettes had a range of AAC systems rated at the available scale from 1 to 7. least 6, for example; five vignettes had over 10% of AAC sys- Descriptions of what the most suitable AAC systems were tems rated at least 6; and one vignette had over 20%. for each vignette are given in Tables 1 and 2, Supplemental 6 E. J. D. WEBB ET AL. Table 3. For each child vignette, the proportions of systems rated at least 5 and at least 6. Percentage of systems rated over Language AAC experience Motivation Trajectory 56 Delayed No experience Motivated (non-AAC) Regress 51.1 0 R> E No experience Motivated (non-AAC) Regress 53.6 0 Delayed No experience Not motivated Regress 70.8 0 Delayed No experience Motivated (non-AAC) Plateau 71.2 0 Delayed Few functions Not motivated Regress 71.9 0 R> E No experience Not motivated Regress 73 0 Delayed No experience Motivated (non-AAC) Progress 73.7 0 R> E No experience Motivated (non-AAC) Plateau 73.7 0 R> E No experience Motivated (non-AAC) Progress 75.2 0 R> E Few functions Not motivated Regress 75.5 0 Delayed No experience Not motivated Plateau 82.5 0 Delayed Few functions Not motivated Plateau 83.6 0 Delayed No experience Not motivated Progress 86.1 0 R> E No experience Not motivated Plateau 86.1 0 Delayed Few functions Not motivated Progress 87.6 0 R> E Few functions Not motivated Plateau 88 0 R> E Few functions Not motivated Progress 88.7 0 R> E Few functions Motivated (AAC) Regress 93.4 0 R> E No experience Motivated (AAC) Regress 94.2 0 Delayed No experience Motivated (AAC) Regress 95.6 0 Delayed Few functions Motivated (AAC) Regress 95.6 0 Delayed Many functions Motivated (AAC) Regress 95.6 0 Delayed No experience Motivated (AAC) Plateau 98.5 0 Delayed Few functions Motivated (AAC) Plateau 98.5 0 R> E No experience Not motivated Progress 86.5 0.365 R> E Few functions Motivated (AAC) Plateau 97.1 2.19 R> E No experience Motivated (AAC) Plateau 97.1 3.28 Delayed Many functions Motivated (AAC) Plateau 98.5 3.28 R> E Many functions Motivated (AAC) Regress 93.4 4.38 Delayed Few functions Motivated (AAC) Progress 95.6 8.76 Delayed No experience Motivated (AAC) Progress 96.7 9.12 R> E Many functions Motivated (AAC) Plateau 97.1 12 R> E Few functions Motivated (AAC) Progress 94.5 12.8 R> E No experience Motivated (AAC) Progress 94.5 13.1 Delayed Many functions Motivated (AAC) Progress 96.7 15 R> E Many functions Motivated (AAC) Progress 95.6 20.4 R> E¼ Receptive language exceeding descriptive language. file. The results are summarized in Figure 4, which illustrates systems varied for different child vignettes. This is not sur- how often a given AAC system feature was part of a child prising, as it is in line with the analysis of participants’ vignette’s most suitable system. Vocabulary sets with staged choices (Webb et al., 2019a) and with previous findings in progression were a feature for 21 out of 36 child vignettes. the literature (Johnson et al., 2006; Light & McNaughton, Only a single child vignette had no pre-provided vocabulary 2014); however, it is an encouraging sign of the face validity set as a feature of a most suitable AAC system. Having fewer of the current study’s approach. than 50 items was only seen as a feature of the most suit- Methods used in the current study allowed for the calcu- able AAC systems for two child vignettes. lation of how participants rated the suitability of 274 AAC For most child vignettes (20), the highest rated AAC sys- systems for each of 36 child vignettes; this, in turn, allowed tem had pragmatic vocabulary organization, with the most a comparison between child vignettes in terms of what frac- suitable system having visual scene organization for only tion of AAC systems were rated above 5 and above 6; how- two child vignettes. When photos were a feature of a most ever, the set of AAC systems used in this survey was not suitable AAC system, this was associated with lower ratings intended to be representative of the characteristics of AAC for those systems, in contrast to text, which was associated systems currently available on the market. There may be no with higher rated most suitable AAC systems. Ideographs available system matching a given description, or there may were not a feature of the most suitable AAC system for any be several different models all having features matching the child vignette. An idiosyncratic layout was a feature of the description; thus, for example, if participants rated 50% of most suitable AAC system for all child vignettes. AAC systems in this survey at least 5 out of 7 for suitability, it does not mean they would give 50% of currently available AAC systems a similar rating. It follows that the proportion Discussion of AAC systems suitable for a given child vignette reported here may not reflect the range of suitable systems that AAC The results show that participants rated the suitability of professionals would consider choosing between in daily prac- AAC systems differently depending on the characteristics of the child vignette they were presented with. There was con- tice. Yet, despite this caveat, the AAC systems presented siderable variation in the fraction of AAC systems that were were considered to be feasible, whether or not they were highly rated, and the features of the most suitable AAC available “off the shelf”, so the results of the current study AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 7 Figure 3. Ratings of the most suitable AAC system for each child vignette. Figure 4. Number of times each AAC system level was part of a child vignette’s most preferred system. do give an indication of the relative numbers of possible excellent, then for all child vignettes at least half of AAC sys- AAC systems that were regarded as acceptable or good for tems were a good fit (the reader may instead choose to different children. interpret 5 as an acceptable rating and 6 as good, for If an average rating of 5 of more out of 7 is taken as example, but the meaning of our discussion is unchanged); good in terms of suitability, and 6 out of 7 taken as however, there was still much variation in the number of 8 E. J. D. WEBB ET AL. AAC systems that were a good fit, from a low of 51.1% to a described as more motivated to communicate via AAC and high of 98.5%. In addition, more variation is revealed in with stronger prognoses for improvement, that implies that terms of excellent systems. Most child vignettes had no AAC there is at least the potential for inequalities in AAC provi- systems that were an excellent fit, yet for one child vignette sion to arise. For some children, fewer AAC systems are well (which could in some ways be considered to have the stron- suited to them, so that barriers to accessing some systems, gest prognosis for improvement) 20.4% of AAC systems were such as cost or requiring a large amount of AAC practitioner considered excellent for suitability. For many child vignettes input to set up, may disproportionately affect them, com- almost all AAC systems were a good fit, yet only a small frac- pared to children for whom many AAC systems are suitable. tion were an excellent fit. In light of this finding, it is encouraging that some dedicated One possible interpretation of most systems being rated funding for AAC systems is available, and Webb et al. good for most child vignettes is that there was a weak (2019b) found that UK AAC professionals ascribed low underlying decision-making rationale. This interpretation importance to cost in their decision-making. However, other would also be consistent with only a small number of evidence suggests cost can play a significant role in AAC vignettes having AAC systems rated over 6: in most cases no professionals’ decision-making in other countries (Van stand-out system (and thus decision rationale) emerged. An Niekerk et al., 2018), and future research could usefully alternative explanation for these observations is that partici- address the extent to which this leads to inequalities in AAC pants rated the suitability of a given AAC system in the con- provision. text of the alternative being no system. If they believed Previous evidence has shown a need for lower learning “some communication is better than none”, then this may demands of AAC systems for some children (Light et al., have led to most AAC systems being rated as good. A fur- 2019; Light, Wilkinson, et al., 2019). This need may have led ther interpretation is that the AAC system attributes used in participants to rate only AAC systems with low learning this study are relatively unimportant compared to other demands highly for some children, explaining some of the attributes not included, although the extensive attribute observed variation in the number of systems with high suit- development process weighs against this possibility. One ability ratings. In particular, it is a plausible explanation as to additional reason behind the finding that many AAC systems why child vignettes predicted to regress in skills and abilities were rated good, but relatively few were rated excellent, is had fewer AAC systems with high suitability ratings. participants’ interpretation of a “suitable match” for a child In line with the previous observation, graphic representa- vignette. It may be that participants interpreted suitable, tion using photos, considered to have lower learning especially on the upper half of the Likert scale, as meaning demands, were commonly a feature of the most suitable adequate. This could have led to participants rating many AAC system for child vignettes predicted to regress in skills AAC systems similarly, rather than making distinctions and abilities and without motivation to communicate using between an adequate and optimal match. If participants did AAC. Photos were associated with having a lower rated most approach the Likert scale tasks in that way, it would lead to suitable AAC system, and text, with greater learning problems in interpreting the study’s results as giving cardinal demands, was associated with having a higher rated most information about strength of preference. However, examin- suitable AAC system. The implication is that for children who ing participants’ responses reveals that they are consistent require an AAC system with low learning demands, not only with them distinguishing between, say, 5 being an adequate were there fewer systems that were a good match, even the match and 7 being an optimal match of AAC system to a most suitable systems were not an ideal match. child vignette. For child vignettes which may broadly be Another factor in how many AAC systems were given described as more motivated to communicate via AAC, and high suitability ratings was whether a child vignette included with a strong prognosis for improvement, participants “was motivated to communicate using AAC” or not. For tended to rate most AAC systems as at least good, but vignettes in which the child was motivated to communicate clearly distinguished some systems as being a better match using AAC, many more AAC systems tended to be rated as for their needs, indicating participants saw a difference good or excellent for suitability. Such motivation was also an between adequate and optimal matching. In addition, suit- important factor in the DCE results, where it led participants ability ratings of AAC systems with greater learning demands to make what could be regarded as more ambitious choices, (large vocabulary, semantic-syntactic organization, etc.) for for example a large vocabulary, or graphic representation more challenging child vignettes were generally lower than using ideographic symbols rather than photos. The current the suitability ratings of basic AAC systems for children moti- vated to use AAC and with strong prognoses for improve- study has given context to this finding by suggesting that, ment. This later observation is consistent with participants although motivation to communicate using AAC was an distinguishing between poor and adequate matches for child important determinant of participants’ choices, the conse- vignettes. quences of such choices were not necessarily large, as partic- ipants regarded many less preferred AAC systems as well suited for motivated children. These findings are in line with Motivation to communicate using AAC and prognosis previous evidence that attitudes toward AAC, and valuing an AAC system are important factors in successfully adopting The vignettes where there were AAC systems ranked over 6 could broadly be described as those where the child was AAC (Johnson et al., 2006; Light & McNaughton, 2014), so AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 9 that children motivated to use AAC are still likely to succeed, an alternative more highly. An alternative explanation is even with an AAC system that is not a perfect match. that in the context of the survey, any degree of layout cus- tomization was classed as idiosyncratic, whereas in practice most AAC professionals wouldn’t necessarily think of them Most suitable AAC systems that way. In any case, this unusual finding should be investi- gated further. The analysis also reveals which AAC systems participants regarded as most suitable for each child vignette. A caveat is that, although combinations of attributes which were Comparison with other findings regarded as unrealistic were excluded by the research team, there is no guarantee that for each AAC system included in Although DCEs are common in healthcare (Soekhai et al., 2019), this is the first study we are aware of that combines the current study, an AAC system exists in the real world choices with ratings. An advantage of this approach is that it that has similar characteristics. There were significant differ- ences between child vignettes in terms of how highly rated gives more information and makes it possible to answer the most suitable AAC systems were, up to 14.3% of the rat- other research questions than with a standard DCE with low ing scale’s available range. This may reflect that, for some extra participant burden and minimal additional resources to children the best available AAC system was not as suitable gather the data. The current study’s novel methods gave to their needs and abilities as for other children. However, quantitative insight into current practice around UK AAC pro- fessionals’ decision-making. Particularly, it and the two linked previous findings have shown that personalizing an off-the- survey studies address the concept of feature matching, i.e., shelf AAC system is an important factor in whether a child matching the characteristics of the child with the most rele- successfully adopts it (Dietz et al., 2012; King et al., 2008; vant AAC system attributes. Their findings can be linked to Light & McNaughton, 2013). Thus it may be that participants evidence from the wider project: Participants included in would have given similar ratings to the most suitable AAC system for all child vignettes if it was clear that they would other elements of the I-ASC study (e.g., Lynch et al., 2019; be personalized to the individual child. Murray et al., 2019) described how they considered and Figure 4 shows the number of times each AAC system made tradeoffs across the decision-making process. At a fea- ture matching level this included consideration of particular level was part of a child vignette’s most preferred system, child characteristics, for example the child’s motivation to and certain characteristics appeared much more often than communicate, their abilities to learn or their likely decline in others. For example, very few child vignettes had a highest learning capacity. Regarding AAC system attributes, we rated AAC system with fewer than 50 vocabulary items. This found consideration of the child’s physical and cognitive is in line with findings from the DCE, which showed that par- characteristics. This included communication aid size and ticipants were always more likely to choose AAC systems weight, which were important for very small children or for with more than 50 vocabulary items than systems with fewer children who were ambulatory. Communication aid appear- than 50, regardless of the child vignette they were choosing ance, voice quality, and reliability were also salient features. for. The result is also consistent with few child vignettes hav- The software attributes prioritized reflected both the needs ing no pre-provided vocabulary sets and many having staged of the child and those providing support. progression as a feature of their most suitable AAC system. A roughly even number of child vignettes had 50–1000 and over 1000 words as part of their most suitable AAC system, Implications so it was not the case that participants believed that more vocabulary items were always better. The different components of research resulted in a general Visual scene was the most preferred mode of vocabulary explanatory model of decision-making in AAC for children. organization for few child vignettes, yet visual scene displays Full details are given in Murray et al. (2020), and a schematic are increasingly common (Beukelman et al., 2021; Wilkinson representation is given in Figure 5. The I-ASC explanatory et al., 2012). This is not necessarily a contradiction, as the model will aid and inform practice by providing a conceptual child vignettes used in this study are intentionally not repre- overview of the linked aspects of decision-making and the sentative of the population of children who would benefit different components of AAC provision for children. It will from AAC. Rather, they represent a wide range of characteris- also stimulate future research by highlighting areas where tics. Thus, it may be that AAC professionals encounter many we currently lack understanding and empirical evidence. children with characteristics similar to the vignettes where In summary, the I-ASC exploration concluded that those visual scene is most suitable, and fewer children similar to charged with the responsibility for proposing specific com- other vignettes. munication aids face a complex task that includes identifying All most preferred systems featured an idiosyncratic lay- the particular child characteristics, access features, and com- out. This uniformity of opinion is surprising. One possible munication aid attributes. A key lesson for practice is that explanation is a form of measurement error. The statistical these must be considered in the recommendations for each model had a limited number of parameters to avoid overfit- child. The challenge is that these are not separate, fixed ting, and it may be that this resulted in some child components of the decision-making process, but are con- vignettes appearing to have idiosyncratic as their most suit- stantly moving, with some being more fluid and others more able layout, whereas in fact participants would have rated stable depending on context as teams reach their decisions. 10 E. J. D. WEBB ET AL. Figure 5. The I-ASC explanatory model of decision making. From Murray et al. (2019). Limitations and future directions the previous choice task; however, incorporating the Likert scale as part of a DCE had many practical advantages, as dis- It is possible to calculate a numerical rating for each AAC cussed above. In addition, recruiting participants to both the system and to test whether any differences are statistically BWS and DCE surveys was difficult given the low numbers of significant. It is not possible, however, to know how mean- AAC professionals in the UK, estimated at around 800 ingful participants considered to be the difference between, (Communication Matters, private correspondence). Thus it for example, an AAC system rated 5 out of 7 and one rated was uncertain whether recruiting participants for a third sur- 6 out of 7. It may be that they considered two such AAC sys- vey would be practical. tems to be very similar, or they could have believed that the In common with all stated preference studies, there is higher rated system would have a significantly positive effect concern about the ecological validity of the survey instru- on a child’s future for many years. Future studies using a ment and whether it captured the relevant aspects of the similar method may wish to investigate giving participants decision-making situation. However, the survey went through guidance as to how to interpret a unit difference in the rat- an extensive development process (Webb et al., 2021)to ing scale. help create as realistic a scenario as possible, and which cap- The ratings for AAC systems are derived from statistical tured the most important aspects of decision-making. A modeling of a limited number of choices for each individual, related limitation is that we could include only a subset of and so participants may have given different responses if the the many characteristics that influence how suitable a match context was changed to rating an AAC system directly; how- an AAC system is for a child. Thus, for example, while we ever, with 36 child vignettes and 274 AAC systems, rating provide information on systematic organization and repre- every system for every vignette would have required partici- sentation, we did not include features related to access. In pants to complete 864 rating tasks, which is unfeasible. In addition, the survey measures what AAC system attributes addition, DCE choices and ratings were gathered at the same participants believed would match well with children. It does time and participants’ ratings may have been influenced by not necessarily capture the effectiveness of the sort of AAC AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 11 systems suggested here to be a suitable match for children, Conclusion and empirical research into whether AAC professionals’ This study complements the earlier BWS and DCE studies, beliefs match up with real-world effectiveness would be use- with all three studies examining the decision-making of AAC ful to investigate in future. professionals choosing AAC systems for children from a dif- It was previously mentioned that the set of AAC systems ferent perspective. There have also been synergies from per- used in the survey was not necessarily reflective of AAC sys- forming the studies together in a single research project. The tems available on the market. Although all systems were results, together with the findings of the wider research pro- feasible, many would not be available off the shelf, and in ject have been used to help create practical resources to practice would require practitioners to adapt an existing AAC help AAC professionals working with children in their every- vocabulary set. The skills, willingness and culture of doing day practice. The suite of resources is freely available at this is likely to vary across practice settings. Additionally the https://iasc.mmu.ac.uk/. availability of AAC systems will vary from place to place (par- ticularly across countries) and the AAC systems and vocabu- laries placed on the market will change over time. Disclosure statement This study had a relatively low sample size compared to No potential conflict of interest was reported by the authors. many similar studies in healthcare (Soekhai et al., 2019). As noted previously, however, the number of AAC professionals Funding in the UK is small, so that the sample size represents a size- able fraction of the target population. This project was funded by the NIHR Health Services and Delivery There was probably some heterogeneity in participants’ Program [project 14/70/153]. The views expressed are those of the opinions and preferences depending on their individual authors and not necessarily those of the NHS, the NIHR or the Department of Health. Stephane Hess acknowledges additional support experiences and familiarity with different AAC systems and by the European Research Council through the consolidator grant children. It is a limitation of our study that we did not collect 615596-DECISIONS. data on what AAC system participants’ were familiar with, and only broad information about what diagnoses they com- ORCID monly encountered; however, it is difficult to collect such detailed information in a short web survey where the focus Edward J. D. Webb http://orcid.org/0000-0001-7918-839X was on preference elicitation tasks. There may also be deci- Yvonne Lynch http://orcid.org/0000-0003-3209-3099 sion-making differences between more experienced AAC pro- Simon Judge http://orcid.org/0000-0001-5119-8094 Juliet Goldbart http://orcid.org/0000-0003-1290-7833 fessionals, as in Sauerwein and Wegner (2020) (though note Liz Moulam http://orcid.org/0000-0003-3810-1037 that in that study, novices were defined as first-year masters Stephane Hess http://orcid.org/0000-0002-3650-2518 students, some of which had not taken an AAC course, Janice Murray http://orcid.org/0000-0001-8809-4256 whereas here 93% of participants had at least one year of professional AAC experience). Future work could examine References heterogeneity in AAC professionals’ decision-making more closely. This includes how decision-making varies among dif- Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10. ferent specialties, which is not possible to explore in the cur- 1109/TAC.1974.1100705 rent study due to 75% of participants having a speech and Beukelman, D., & Light, J. (2020). Augmentative and alternative communi- language therapy background. For example, speech and lan- cation for children and adults. (5th ed.). Paul H. Brookes Publishing Co. guage therapists may have interpreted regression as a loss Beukelman, D. R., Thiessen, A., & Fager, S. K. (2021). Personalization of of language skills, whereas occupational therapists inter- visual scene displays: Preliminary investigations of adults with apha- sia, typical females across the age-span, and young adult males and preted it as a loss of physical abilities or vision, and made females. Topics in Language Disorders, 41(3), e1–E11. https://doi.org/ different decision accordingly. 10.1097/TLD.0000000000000256 There is much scope for future research to build on the Cheung, K. L., Wijnen, B. F., Hollin, I. L., Janssen, E. M., Bridges, J. F., current study. For example, it would be useful to examine Evers, S. M., & Hiligsmann, M. (2016). Using best–worst scaling to whether the findings about the suitability of hypothetical investigate preferences in health care. PharmacoEconomics, 34(12), 1195–1209. https://doi.org/10.1007/s40273-016-0429-5 AAC systems concur with AAC professionals’ opinions about Choi, B. C., & Pak, A. W. (2006). Multidisciplinarity, interdisciplinarity and the suitability of real life systems. In addition, the current transdisciplinarity in health research, services, education and policy: 1. study highlighted areas in which inequalities in provision Definitions, objectives, and evidence of effectiveness. Clinical and could occur, and in future, it could be examined whether Investigative Medicine. Medecine Clinique et Experimentale, 29(6), 351– such inequalities are found. Finally, it would be fruitful to Dietz, A., Quach, W., Lund, S. K., & Mckelvey, M. (2012). AAC assessment explore whether AAC professionals’ opinions about the suit- and clinical-decision making: The impact of experience. Augmentative ability of AAC systems are in agreement with other stake- and Alternative Communication (Baltimore, Md. : 1985), 28(3), 148–159. holders such as people who use AAC and their families. This https://doi.org/10.3109/07434618.2012.704521 latter issue is of particular importance given the likely impact Enderby, P., Judge, S., Creer, S., & John, A. (2013). Examining the need on how motivated a child is to use an AAC system, and on for and provision of AAC methods in the UK. Advances in Clinical how motivated a family is to provide support. Neuroscience & Rehabilitation, 13,20–23. 12 E. J. D. WEBB ET AL. Fried-Oken, M., Mooney, A., & Kinsella, M. (2019). Cognitive Demands Light, J., McNaughton, D., & Caron, J. (2019). New and emerging AAC Checklist for Augmentative and Alternative Communication technology supports for children with complex communication needs (CDC4AAC). Presentation at the Assistive Technology Industry and their communication partners: State of the science and future Association (ATIA) Annual Conference, Orlando, FL. research directions. Augmentative and Alternative Communication Geytenbeek, J. J., Vermeulen, R. J., Becher, J. G., & Oostrom, K. J. (2015). (Baltimore, Md. : 1985), 35(1), 26–41. https://doi.org/10.1080/07434618. Comprehension of spoken language in non-speaking children with 2018.1557251 severe cerebral palsy: An explorative study on associations with Light, J., Wilkinson, K. M., Thiessen, A., Beukelman, D. R., & Fager, S. K. motor type and disabilities. Developmental Medicine and Child (2019). Designing effective AAC displays for individuals with develop- Neurology, 57(3), 294–300. https://doi.org/10.1111/dmcn.12619 mental or acquired disabilities: State of the science and future Gross, J. (2010). Augmentative and alternative communication: A report research directions. Augmentative and Alternative Communication on provision for children and young people in England. Office of the (Baltimore, Md. : 1985), 35(1), 42–55. https://doi.org/10.1080/07434618. Communication Champion. 2018.1558283 Hajjar, D. J., Mccarthy, J. W., Benigno, J. P., & Chabot, J. (2016). You get Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: more than you give”: Experiences of community partners in facilitat- Analysis and applications., Cambridge University Press. ing active recreation with individuals who have complex communica- Lund, S. K., & Light, J. (2006). Long-term outcomes for individuals who tion needs. Augmentative and Alternative Communication (Baltimore, use augmentative and alternative communication: Part I–What is a Md. : 1985), 32(2), 131–142. https://doi.org/10.3109/07434618.2015. “good. Augmentative and Alternative Communication (Baltimore, Md. : 1136686 1985), 22(4), 284–299. https://doi.org/10.1080/07434610600718693 Hemsley, B., & Murray, J. (2015). Distance and proximity: Research on Lund, S. K., Quach, W., Weissling, K., Mckelvey, M., & Dietz, A. J. (2017). social media connections in the field of communication disability. Assessment with children who need augmentative and alternative Disability and Rehabilitation, 37(17), 1509–1510. https://doi.org/10. communication (AAC): Clinical decisions of AAC specialists. Language, 3109/09638288.2015.1057031 Speech, and Hearing Services in Schools, 48(1), 56–68. https://doi.org/ Hess, S., & Palma, D. (2019). Apollo: A flexible, powerful and customis- 10.1044/2016_LSHSS-15-0086 able freeware package for choice model estimation and application. Lynch, Y., Murray, J., Moulam, L., Meredith, S., Goldbart, J., Smith, M., Journal of Choice Modelling, 32, 100170. https://doi.org/10.1016/j.jocm. Batorowicz, B., Randall, N., & Judge, S. (2019). Decision-making in 2019.100170 communication aid recommendations in the UK: Cultural and context- Hynan, A., Goldbart, J., & Murray, J. (2015). A grounded theory of inter- ual influencers. Augmentative and Alternative Communication net and social media use by young people who use augmentative (Baltimore, Md. : 1985), 35(3), 180–192. https://doi.org/10.1080/ and alternative communication (AAC). Disability and Rehabilitation, 07434618.2019.1599066 37(17), 1559–1575. https://doi.org/10.3109/09638288.2015.1056387 McFadd, E., & Wilkinson, K. (2010). Qualitative analysis of decision mak- Johnson, J. M., Inglebret, E., Jones, C., & Ray, J. (2006). Perspectives of ing by speech-language pathologists in the design of aided visual dis- speech language pathologists regarding success versus abandonment plays. Augmentative and Alternative Communication, 26(2), 136–147. of AAC. Augmentative and Alternative Communication, 22(2), 85–99. https://doi.org/10.3109/07434618.2010.481089 https://doi.org/10.1080/07434610500483588 Murray, J., Lynch, Y., Goldbart, J., Moulam, L., Judge, S., Webb, E., Jayes, Judge, S., Enderby, P., Creer, S., & John, A. (2017). Provision of powered M., Meredith, S., Whittle, H., Randall, N., Meads, D., & Hess, S. (2020). communication aids in the United Kingdom. Augmentative and The decision-making process in recommending electronic communi- Alternative Communication (Baltimore, Md. : 1985), 33(3), 181–187. cation aids for children and young people who are non-speaking: The https://doi.org/10.1080/07434618.2017.1347960 I-ASC mixed-methods study. Health Services and Delivery Research, Judge, S., Randall, N., Goldbart, J., Lynch, Y., Moulam, L., Meredith, S., & 8(45), 1–158. https://doi.org/10.3310/hsdr08450 Murray, J. (2020). The language and communication attributes of Murray, J., Lynch, Y., Meredith, S., Moulam, L., Goldbart, J., Smith, M., graphic symbol communication aids–a systematic review and narra- Randall, N., & Judge, S. (2019). Professionals’ decision-making in rec- tive synthesis. Disability and Rehabilitation. Assistive Technology, 15(6), ommending communication aids in the UK: Competing considera- 652–662. https://doi.org/10.1080/17483107.2019.1604828 tions. Augmentative and Alternative Communication, 35(3), 167–179. King, G., Batorowicz, B., & Shepherd, T. A. (2008). Expertise in research- https://doi.org/10.1080/07434618.2019.1597384 informed clinical decision making: Working effectively with families of NHS England. (2016). Guidance for commissioning AAC services and children with little or no functional speech. Evidence-Based equipment. https://www.england.nhs.uk/commissioning/wp-content/ Communication Assessment and Intervention, 2(2), 106–116. https://doi. uploads/sites/12/2016/03/guid-comms-aac.pdf org/10.1080/17489530802296897 Royal College of Speech and Language Therapists (2009). Resource man- Kuhfeld, W. F., Tobias, R. D., & Garratt, M. J. (1994). Efficient experimental ual for commissioning and planning services for SLCN. Royal College of design with marketing research applications. Journal of Marketing Speech and Language Therapists. Research, 31(4), 545–557. https://doi.org/10.1177/0022243794031 Ryan, S. E., Shepherd, T., Renzoni, A. M., Anderson, C., Barber, M., 00408 Kingsnorth, S., & Ward, K. (2015). Towards advancing knowledge Light, J., & McNaughton, D. (2013). Putting people first: Re-thinking the translation of AAC outcomes research for children and youth with role of technology in augmentative and alternative communication complex communication needs. Augmentative and Alternative intervention. Augmentative and Alternative Communication (Baltimore, Communication, 31(2), 137–147. https://doi.org/10.3109/07434618. Md. : 1985), 29(4), 299–309. https://doi.org/10.3109/07434618.2013. 2015.1030038 848935 Sauerwein, A. M., & Wegner, J. R. (2020). Clinical reasoning skills in AAC Light, J., & McNaughton, D. (2014). Communicative competence for indi- intervention planning: Investigating the expert-novice gap. Teaching viduals who require augmentative and alternative communication: A and Learning in Communication Sciences & Disorders, 4(2), 7. https:// new definition for a new era of communication? Augmentative and doi.org/10.30707/TLCSD4.2/XNDO8764 Alternative Communication (Baltimore, Md. : 1985), 30(1), 1–18. https:// Schlosser, R. W., & Wendt, O. (2008). Effects of augmentative and alterna- doi.org/10.3109/07434618.2014.885080 tive communication intervention on speech production in children Light, J., McNaughton, D., Beukelman, D., Fager, S. K., Fried-Oken, M., with autism: A systematic review. American Journal of Speech- Jakobs, T., & Jakobs, E. (2019). Challenges and opportunities in aug- Language Pathology, 17(3), 212–230. https://doi.org/10.1044/1058- mentative and alternative communication: Research and technology 0360(2008/021) development to enhance communication and participation for indi- Soekhai, V., De Bekker-Grob, E. W., Ellis, A. R., & Vass, C. M. (2019). viduals with complex communication needs. Augmentative and Discrete choice experiments in health economics: Past, present and Alternative Communication (Baltimore, Md. : 1985), 35(1), 1–12. https:// future. PharmacoEconomics, 37(2), 201–226. https://doi.org/10.1007/ doi.org/10.1080/07434618.2018.1556732 s40273-018-0734-2 AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 13 Sundqvist, A., & Ronnberg, € J. (2010). A qualitative analysis of email inter- Webb, E. J., Meads, D., Lynch, Y., Judge, S., Randall, N., Goldbart, J., Meredith, S., Moulam, L., Hess, S., & Murray, J. (2021). Attribute selec- actions of children who use augmentative and alternative communi- tion for a discrete choice experiment incorporating a best-worst scal- cation. Augmentative and Alternative Communication (Baltimore, Md. : ing survey. Value in Health : The Journal of the International Society for 1985), 26(4), 255–266. https://doi.org/10.3109/07434618.2010.528796 Pharmacoeconomics and Outcomes Research, 24(4), 575–584. https:// Thistle, J. J., & Wilkinson, K. M. (2015). Building evidence-based practice doi.org/10.1016/j.jval.2020.10.025 in AAC display design for young children: Current practices and Webb, E. J., Meads, D., Lynch, Y., Randall, N., Judge, S., Goldbart, J., future directions. Augmentative and Alternative Communication Meredith, S., Moulam, L., Hess, S., & Murray, J. (2019b). What’s import- (Baltimore, Md. : 1985), 31(2), 124–136. https://doi.org/10.3109/ ant in AAC decision making for children? Evidence from a best–worst 07434618.2015.1035798 scaling survey. Augmentative and Alternative Communication Van Niekerk, K., Dada, S., Tonsing, € K., & Boshoff, K. (2018). Factors per- (Baltimore, Md. : 1985), 35(2), 80–94. https://doi.org/10.1080/07434618. ceived by rehabilitation professionals to influence the provision of 2018.1561750 assistive technology to children: A systematic review. Physical & Wilkinson, K. M., Light, J., & Drager, K. (2012). Considerations for the Occupational Therapy in Pediatrics, 38(2), 168–189. https://doi.org/10. composition of visual scene displays: Potential contributions of infor- 1080/01942638.2017.1337661 mation from visual and cognitive sciences. Augmentative and Webb, E. J., & Hess, S. (2021). Joint modelling of choice and rating data: Alternative Communication (Baltimore, Md. : 1985), 28(3), 137–147. Theory and examples. Journal of Choice Modelling, 40, 100304. https:// https://doi.org/10.3109/07434618.2012.704522 doi.org/10.1016/j.jocm.2021.100304 Williams, M., Beukelman, D., & Ullman, C. (2012). AAC text messaging. Webb, E. J., Lynch, Y., Meads, D., Judge, S., Randall, N., Goldbart, J., Perspectives on Augmentative and Alternative Communication, 21(2), Meredith, S., Moulam, L., Hess, S., & Murray, J. (2019a). Finding the 56–59. https://doi.org/10.1044/aac21.2.56 best fit: Examining the decision-making of augmentative and alterna- Williams, M. B., Krezman, C., & McNaughton, D. J. (2008). Reach for the tive communication professionals in the UK using a discrete choice stars”: Five principles for the next 25 years of AAC. Augmentative and experiment. BMJ Open, 9(11), e030274. https://doi.org/10.1136/ Alternative Communication, 24(3), 194–206. https://doi.org/10.1080/ bmjopen-2019-030274 08990220802387851 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Augmentative and Alternative Communication Taylor & Francis

Something for everybody? Assessing the suitability of AAC systems for children using stated preference methods

Loading next page...
 
/lp/taylor-francis/something-for-everybody-assessing-the-suitability-of-aac-systems-for-keEecPTtC0

References (52)

Publisher
Taylor & Francis
Copyright
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ISSN
1477-3848
eISSN
0743-4618
DOI
10.1080/07434618.2023.2206582
Publisher site
See Article on Publisher Site

Abstract

AUGMENTATIVE AND ALTERNATIVE COMMUNICATION https://doi.org/10.1080/07434618.2023.2206582 RESEARCH ARTICLE Something for everybody? Assessing the suitability of AAC systems for children using stated preference methods a,b a,b c d d Edward J. D. Webb , David Meads , Yvonne Lynch , Nicola Randall , Simon Judge , e e e b,f e Juliet Goldbart , Stuart Meredith , Liz Moulam , Stephane Hess and Janice Murray a b c Leeds Institute of Health Sciences, University of Leeds, Leeds, UK; Choice Modelling Centre, University of Leeds, Leeds, UK; School of Linguistic, Speech and Communication Sciences, Trinity College Dublin, Dublin, Ireland; Assistive Technology Team, Barnsley Hospital NHS e f Foundation Trust, Barnsley, UK; Faculty of Health and Education, Manchester Metropolitan University, Manchester, UK; Institute for Transport Studies, University of Leeds, Leeds, UK ABSTRACT ARTICLE HISTORY Received 21 January 2022 Little is known about what features of AAC systems are regarded by AAC professionals as more suit- Revised 2 September 2022 able for children with different characteristics. A survey was conducted in which participants rated the Accepted 13 December 2022 suitability of hypothetical AAC systems on a Likert scale from 1 (very unsuitable)to 7 (very suitable) alongside a discrete choice experiment. The survey was administered online to 155 AAC professionals KEYWORDS in the United Kingdom of Great Britain and Northern Ireland. Statistical modeling was used to estimate Children; clinical decision- how suitable 274 hypothetical AAC systems were for each of 36 child vignettes. The proportion of making; discrete choice AAC systems rated at least 5 out of 7 for suitability varied from 51.1% to 98.5% for different child experiment; likert scale; vignettes. Only 12 out of 36 child vignettes had any AAC systems rated at least 6 out of 7 for suitabil- stated preferences ity. The features of the most suitable AAC system depended on the characteristics of the child vignette. The results show that, while every child vignette had several systems that had a good suit- ability rating, there were variations, that could potentially lead to inequalities in provision. Augmentative and alternative communication (AAC) can representation would better suit the child’s future needs improve the lives of many people with communication diffi- given their progress. For more details, see Beukelman and culties (Hajjar et al., 2016; Ryan et al., 2015; Schlosser & Light (2020). Wendt, 2008). For children, provision of AAC is especially Research has revealed some important factors in AAC pro- important, as it can affect their social participation as well as fessionals’ decision-making such as a child’s preferences and their development and learning, having an impact on the family circumstances (Enderby et al., 2013; Geytenbeek et al., rest of their lives (Lund & Light, 2006; Ryan et al., 2015). In 2015; Thistle & Wilkinson, 2015). Assessing the cognitive demands of a given AAC system places on a child is another recent years, the expectations of people who use AAC to participate in all aspects of society has increased (Hemsley & important factor (Fried-Oken et al., 2019). There are also guidelines for how AAC services should be organized (Choi & Murray, 2015; Hynan et al. 2015; Light, McNaughton et al. 2019; Sundqvist & Ronnberg, 2010; Williams et al., 2008, Pak, 2006; National Health Service (NHS) England, 2016; 2012). Royal College of Speech and Language Therapists, 2009). Many different AAC systems exist, with very different fea- Some research has been conducted on AAC professionals’ tures. Children may benefit from AAC due to a wide range of decision-making. For example, it has been shown that pro- reasons and may have a variety of conditions such as cere- fessionals with different backgrounds (Dietz et al., 2012) and bral palsy and autism spectrum condition. Children with the levels of experience (Sauerwein & Wegner, 2020) differ in same diagnosis each have disparate needs, abilities, and per- their consideration of factors. Still, little is known about the sonal circumstances. Selecting a suitable AAC system for a details of how AAC professionals make decisions, or what child is thus a highly complex task, requiring the balancing features lead AAC professionals to judge AAC systems as a of many different competing concerns, and the process is suitable match for children with different characteristics unique to each child (Dietz et al., 2012; Lund et al., 2017; (Dietz et al., 2012; McFadd & Wilkinson, 2010; Ryan et al., Lynch et al., 2019). For example, matching might involve 2015). assessing whether a child is more motivated by vocabulary The current study examined AAC professionals’ judgment represented by photos or text. This consideration would and decision-making for children, specifically how suitable have to be balanced against which mode of graphical they believe different AAC systems are for different children. CONTACT Edward J. D. Webb e.j.d.webb@leeds.ac.uk Leeds Institute of Health Sciences, University of Leeds, Worsley Building, Clarendon Way, Leeds, LS2 9NL, UK Supplemental data for this article can be accessed online at https://doi.org/10.1080/07434618.2023.2206582 2023 International Society for Augmentative and Alternative Communication 2 E. J. D. WEBB ET AL. Figure 1. Schematic representation of the relationship between the current study and the wider I-ASC project. (By AAC system we refer in this manuscript to aided systems different AAC systems to be for a child, and how do their which may be either high-tech or low-tech.) Examining AAC judgements change depending on child characteristics? This professionals’ judgment and decision-making addresses the study adds information about strength of preference, reveal- previously noted knowledge gap and allows an examination ing not only what AAC system an AAC professional would of whether a gap exists between research and practice. The choose for a child, but also how suitable they believe the study also makes it possible to reflect on how improvements system to be for that child. The context for the current study is the UK, where it is may be made to current practice and service structures. The current study is part of three linked studies that used estimated that 1 in 200 children could benefit from AAC different methods to address the topic of AAC professionals’ (Enderby et al., 2013; Gross, 2010; Judge et al., 2017). There judgment and decision-making from different angles. The is some variation across the UK in how children are allocated principal research question for the first study (Webb et al. AAC systems, but in general children’s needs, abilities and 2019b) was: What is the relative importance of AAC system circumstances are assessed by a multidisciplinary team of attributes and child characteristics in AAC professionals’ deci- AAC professionals. Final recommendations are made with sion-making in daily practice? The study revealed how input from children and their support network about their important different factors were in AAC professionals’ daily preferences and opinions about the potential options. The practice; in other words how important specific factors were composition of the multidisciplinary team varies, and can averaged over the case-mix AAC professionals see. For include speech and language therapists, occupational thera- example, it revealed that interface-related AAC system attrib- pists, and teachers (Lynch et al., 2019; NHS England, 2016). utes were relatively more important than hardware attributes The current study was part of a wider research project in professionals’ daily practice. Averaged over the case-mix entitled Identifying Appropriate Symbol Communication aids that they saw, participants also judged children’s cognitive for children who are non-speaking: enhancing clinical deci- and learning abilities to be more important than their phys- sion-making (I-ASC) (Murray et al., 2020). The project used a ical features. The second study (Webb et al., 2019a) had the variety of research methods (Judge et al., 2020; Lynch et al., principal research question: When choosing an AAC system, 2019; Murray et al., 2019) to study AAC provision for children what tradeoffs do AAC professionals make between system in the UK. The evidence produced by the project has been attributes, and how do such tradeoffs change depending on used in the creation of a suite of resources for AAC profes- the characteristics of the child? This study revealed how AAC sionals and other stakeholders to support best practice, professionals make decisions when choosing for an individ- which is available for free at https://iasc.mmu.ac.uk/. See ual child. For example, it showed that children’s motivation Figure 1 for a schematic overview of the relations between to communicate using AAC and predicted future abilities the current study and the different components of the I-ASC had a greater influence on how participants traded-off differ- project. ent AAC system attributes than their language ability and The current study contributed to the wider project by showing how suitable AAC professionals judged different previous experience with AAC. Finally, the current study has the principal research ques- AAC systems to be for children with different characteristics. tion: How do AAC professionals judge the suitability of It examined whether, in the opinion of survey participants, AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 3 there was an AAC system which suited the needs of every the I-ASC project’s website and social media. The survey was individual child. In addition, it complemented investigations open for responses from 20 October 2017 until 4 March 2018. as to what systems AAC professionals would choose, as they A total of 172 people submitted complete responses, of which 155 completed the DCE and Likert scale tasks. may not necessarily choose what they believe to be the Participant demographics are summarized in Table 2. The most suitable AAC system for a child. For example, the most demographics of participants who did not complete the DCE suitable AAC system may not be chosen due to resource or Likert scale tasks were largely similar to those who did; constraints such as cost or instruction time. however, they were slightly older on average, at 46 years, and were more likely to have a professional background as Method an occupational therapist (n¼ 5, 29.4% compared to n¼ 11, 7%) than a background in speech and language therapy This study used data collected from the same participants as (n¼ 8, 47.1% compared to n¼ 117, 75.5%). Webb et al. (2019a), as well as the same procedures. Materials and measures Research design The study used a discrete choice experiment survey with The study used a survey design that combined a discrete additional Likert scale questions. The survey development choice experiment (DCE) and Likert scale ratings. The current and administration are described in the section that follows. study analyzed data from the Likert scale tasks which were asked after each DCE task in which participants rated how good a match an AAC system was for a child vignette on a Procedures scale from 1 (very unsuitable)to 7 (very suitable). The current In Webb et al. (2019b), lists of 18 AAC system attributes and study is concerned with cardinal measures of an AAC sys- 19 child characteristics were constructed using information tem’s suitability for a child. from systematic reviews of the literature (Judge et al., 2020), Likert scale ratings allowed the research question of how input from an expert panel as well as focus groups and inter- suitable a match different AAC systems are for different chil- views with AAC professionals, people who use AAC, their dren to be addressed. The innovative method of combining families and other stakeholders (Lynch et al., 2019; Murray a Likert scale with a DCE enabled the estimation of suitability et al., 2019). These lists were then included in a survey that ratings for many more AAC system-child combinations than used a method called best-worst scaling Case 1 (BWS) would be feasible for survey participants to rate individually. (Cheung et al., 2016) and that was administered to 93 AAC The study received ethical approval from an NHS professionals. Research Ethics Committee (REC reference 6/NW/0165). From the BWS attributes, five AAC system attributes and Participants gave informed consent. four child attributes were selected for inclusion in a DCE/Likert scale survey based on the following criteria: (a) attributes formed coherent descriptions of AAC systems/chil- Participants dren, (b) attributes reflected the specific aims of the I-ASC The target population was AAC professionals working in the project, (c) most attributes were of high relative importance UK who contributed to AAC decision-making for children. according to the BWS survey results, and (d) the number of Participants were recruited using the email lists of the I-ASC attributes was not so large as to overburden DCE survey project and Communication Matters (a UK-wide AAC charity respondents. The final list of attributes and levels for the and chapter of the International Society for Augmentative DCE is given in Table 1. and Alternative Communication), as well as project members’ The statistical design of the DCE was constructed using personal contacts. In addition, the survey was advertised on Ngene (ChoiceMetrics). This software package selected a Table 1. Attributes and levels for discrete choice experiment and likert scale survey. Attribute Levels Children Receptive and expressive language Delayed/ Receptive language exceeding expressive language Communication ability with AAC No previous AAC experience/ Able to use AAC for a few communicative functions/ Able to use AAC for a range of communicative functions Child’s determination and persistence Does not appear motivated to communicate through any methods and means/ Motivated to communicate through symbol communication systems/ Only motivated to communicate through methods other than symbol communication Predicted future skills and abilities Regression/ Plateau/ Progression AAC systems Vocabulary sets No vocabulary set / Fixed vocabulary set/ Vocabulary set with staged progression/ Size of vocabulary Up to 50 vocabulary items /50–1000 vocabulary items/ More than 1000 vocabulary items Consistency of layout Consistency of some aspects of layout / Consistency of all aspects of layout/ Idiosyncratic layout Type of vocabulary organization Visual scene / Taxonomic/ Semantic-syntactic/ Pragmatic Graphic representation Photos / Pictographic symbol set/ Ideographic symbol system (with rules or encoding)/ Text Indicates baseline level. 4 E. J. D. WEBB ET AL. Table 2. Participant demographics (N¼ 172). answer only demographic questions and were not shown Completed DCE Did not complete DCE the DCE or Likert scale tasks. The survey was administered online by a market research Characteristic (n¼ 155) % (n¼ 17) % company. Before starting the survey, participants were given Age 40.2 10.9 46.1 10.9 Years of experience 11.4 9.15 11.6 9.87 instructions, including attributes/characteristics and levels Female 140 90.3 15 88.2 descriptions. In each DCE task, participants were shown a White ethnicity 137 88.4 12 70.6 child vignette formed from the set of child attributes. For Professional background Speech and language therapist 117 75.5 8 47.1 example: Occupational therapist 11 7.1 5 29.4 Assistive technology specialist 5 3.23 0 0 Child A has receptive language exceeding expressive language. Teacher 11 7.1 3 17.6 Child A is able to use AAC for a few communicative functions. Other 12 7.74 0 0 Child A is motivated to communicate through symbol Common diagnoses communication systems. Child A is predicted to plateau in skills Autism spectrum 101 65.2 12 70.6 and abilities. Physical 128 82.6 12 70.6 Dyspraxia 12 7.74 2 11.8 In each task, participants were shown three hypothetical Intellectual disability/delay 107 69 11 64.7 Neurological 39 25.2 6 35.3 AAC systems described in term of the attributes in Table 1 Speech/language disorder 19 12.3 3 17.6 and asked which they would choose for the child vignette. Syndromes 56 36.1 5 29.4 An example choice task, including example AAC systems, is Location North West England 20 12.9 2 11.8 shown in Figure 2. After making their choice, participants North East England 5 3.23 1 5.88 were asked to rate how suitable their chosen AAC system Yorkshire and Humber 22 14.2 1 5.88 was for the child vignette using a Likert scale that ranged West Midlands 12 7.74 1 5.88 East Midlands 11 7.1 1 5.88 from 1 (very unsuitable)to 7 (very suitable). Participants were East of England 14 9.03 3 17.6 shown three randomly chosen child vignettes. For each child South West England 8 5.16 0 0 East England 32 20.6 3 17.6 vignette they completed four DCE and Likert scale tasks, London 18 11.6 4 23.5 meaning a total of 12 DCE and Likert scale tasks. An example Northern Ireland 5 3.23 0 0 survey can be found in the supplementary material of Webb North Wales 3 1.94 0 0 Wales 5 3.23 0 0 et al. (2019a). Further details about survey development are Mid-Wales 3 1.94 0 0 reported in Webb et al. (2021) and Webb et al. (2019a). Southern Scotland 7 4.52 0 0 Central Scotland 11 7.1 1 5.88 Northern Scotland 6 3.87 0 0 Non-UK 4 2.58 0 0 Statistical Analysis DCE: discrete choice experiment. Analysis of responses used a random utility theory frame- Mean and standard deviation. work (Louviere et al., 2000) that assumed individuals assigned a utility to each option. The utility of each option design that maximized D-efficiency, which may be thought was modeled as depending partly on the attributes of AAC of as a measure of how much information it is possible to systems as well as having a random component, represent- extract from survey responses (Kuhfeld et al., 1994). The ing all aspects of decision-making not explicitly captured by design had 60 tasks which were divided into five blocks of the model. Individuals were then assumed to choose the 12. Each participant was randomly allocated to answer a AAC system with the highest utility, and rated AAC systems block of 12 questions, with random allocations of blocks and higher if they had a higher utility. child vignettes independent of each other. It was possible to Ratings and choices were analyzed jointly using choice- form 54 child vignettes and 432 AAC systems from the sets ordered logit models (Webb & Hess, 2021) that had a set of of attributes. A total of 18 child vignettes and 158 AAC sys- parameters representing how individuals made their deci- tems were identified as representing unrealistic combinations sions. Statistical techniques were used to find the parameters and excluded from being used in the survey according to that maximized the probability of observing the choice and the judgements of authors with AAC expertise (an example ratings participants made. The full model with parameters is that it would be unrealistic to have a vocabulary set with for every interaction between AAC system and child attrib- staged progression with fewer than 50 vocabulary items). utes had too many parameters to estimate robustly. The survey was piloted with five AAC professionals. In Therefore, an iterative process was used in which a series of response to feedback, small changes were made to wording models with only one parameter were estimated. The param- and visual presentation to improve clarity. Piloting revealed eter that contributed most to explaining how participants that some AAC professionals did not have enough input into made their decision was selected for inclusion. A further ser- decision-making in their daily practice to meaningfully ies of models with two parameters were then estimated, and engage with the DCE/Likert scale tasks. To address this, at again the parameter that contributed most to explaining par- the beginning of the survey, participants answered the ques- ticipants’ decision-making was selected. This continued until tion “I confirm my work involves assessing children for aided all parameters were included. The final model was then AAC systems and I contribute to the decision-making in rela- selected using the Akaike information criterion (Akaike, tion to the language and vocabulary organization within AAC systems.” Those who responded no were directed to 1974), a measure of how well a model fits a dataset (see AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 5 Figure 2. Screenshot of example discrete choice experiment task. Figure 1, Supplemental file, for technical details of the model Of the 24 child vignettes without an AAC system, 11 rated estimation). 6 or higher were predicted to regress in skills and abilities, The final model was used to predict participants’ ratings whereas eight were predicted to plateau and four were pre- for every AAC system for every child vignette. It was then dicted to progress. In contrast, out of the 12 vignettes with an AAC system rated at least 6, seven were predicted to pro- calculated for each child vignette what percentage of AAC systems had a rating of at least 5 out of 7, and what per- gress in skills and abilities, four to plateau and one was pre- dicted to regress. All but one of the 12 child vignettes with centage had a rating of at least 6 out of 7. All model estima- at least one AAC system rated 6 or above for suitability were tion was carried out using the Apollo choice modeling motivated to communicate using AAC. package for R (Hess & Palma, 2019). Figure 3 shows how the most suitable AAC systems for each child vignette were rated. The vignette “delayed recep- tive and expressive language, no AAC experience, not moti- Results vated to communicate by any means, expected to regress in The raw results for model estimation are given in Table 1, skills and abilities” had the lowest rated most suitable AAC Supplemental file. Table 3 gives for each child vignette the system, at 5.62. The vignette “receptive language exceeding percentage of all 274 AAC systems included in the survey expressive language, experience of using AAC for a range of that were rated above 5 and above 6. All child vignettes had functions, motivated to communicate using AAC, expected at least 51.1% of AAC systems rated above 5, and for 19 out to progress in skills and abilities” had the highest rated most of 36 this percentage was above 90%. For 24 out of 36 child suitable system, at 6.62. The difference of 1 between the rat- vignettes, no AAC system was rated at 6 or above. However, ings of the most suitable AAC system represents 14.3% of some child vignettes had a range of AAC systems rated at the available scale from 1 to 7. least 6, for example; five vignettes had over 10% of AAC sys- Descriptions of what the most suitable AAC systems were tems rated at least 6; and one vignette had over 20%. for each vignette are given in Tables 1 and 2, Supplemental 6 E. J. D. WEBB ET AL. Table 3. For each child vignette, the proportions of systems rated at least 5 and at least 6. Percentage of systems rated over Language AAC experience Motivation Trajectory 56 Delayed No experience Motivated (non-AAC) Regress 51.1 0 R> E No experience Motivated (non-AAC) Regress 53.6 0 Delayed No experience Not motivated Regress 70.8 0 Delayed No experience Motivated (non-AAC) Plateau 71.2 0 Delayed Few functions Not motivated Regress 71.9 0 R> E No experience Not motivated Regress 73 0 Delayed No experience Motivated (non-AAC) Progress 73.7 0 R> E No experience Motivated (non-AAC) Plateau 73.7 0 R> E No experience Motivated (non-AAC) Progress 75.2 0 R> E Few functions Not motivated Regress 75.5 0 Delayed No experience Not motivated Plateau 82.5 0 Delayed Few functions Not motivated Plateau 83.6 0 Delayed No experience Not motivated Progress 86.1 0 R> E No experience Not motivated Plateau 86.1 0 Delayed Few functions Not motivated Progress 87.6 0 R> E Few functions Not motivated Plateau 88 0 R> E Few functions Not motivated Progress 88.7 0 R> E Few functions Motivated (AAC) Regress 93.4 0 R> E No experience Motivated (AAC) Regress 94.2 0 Delayed No experience Motivated (AAC) Regress 95.6 0 Delayed Few functions Motivated (AAC) Regress 95.6 0 Delayed Many functions Motivated (AAC) Regress 95.6 0 Delayed No experience Motivated (AAC) Plateau 98.5 0 Delayed Few functions Motivated (AAC) Plateau 98.5 0 R> E No experience Not motivated Progress 86.5 0.365 R> E Few functions Motivated (AAC) Plateau 97.1 2.19 R> E No experience Motivated (AAC) Plateau 97.1 3.28 Delayed Many functions Motivated (AAC) Plateau 98.5 3.28 R> E Many functions Motivated (AAC) Regress 93.4 4.38 Delayed Few functions Motivated (AAC) Progress 95.6 8.76 Delayed No experience Motivated (AAC) Progress 96.7 9.12 R> E Many functions Motivated (AAC) Plateau 97.1 12 R> E Few functions Motivated (AAC) Progress 94.5 12.8 R> E No experience Motivated (AAC) Progress 94.5 13.1 Delayed Many functions Motivated (AAC) Progress 96.7 15 R> E Many functions Motivated (AAC) Progress 95.6 20.4 R> E¼ Receptive language exceeding descriptive language. file. The results are summarized in Figure 4, which illustrates systems varied for different child vignettes. This is not sur- how often a given AAC system feature was part of a child prising, as it is in line with the analysis of participants’ vignette’s most suitable system. Vocabulary sets with staged choices (Webb et al., 2019a) and with previous findings in progression were a feature for 21 out of 36 child vignettes. the literature (Johnson et al., 2006; Light & McNaughton, Only a single child vignette had no pre-provided vocabulary 2014); however, it is an encouraging sign of the face validity set as a feature of a most suitable AAC system. Having fewer of the current study’s approach. than 50 items was only seen as a feature of the most suit- Methods used in the current study allowed for the calcu- able AAC systems for two child vignettes. lation of how participants rated the suitability of 274 AAC For most child vignettes (20), the highest rated AAC sys- systems for each of 36 child vignettes; this, in turn, allowed tem had pragmatic vocabulary organization, with the most a comparison between child vignettes in terms of what frac- suitable system having visual scene organization for only tion of AAC systems were rated above 5 and above 6; how- two child vignettes. When photos were a feature of a most ever, the set of AAC systems used in this survey was not suitable AAC system, this was associated with lower ratings intended to be representative of the characteristics of AAC for those systems, in contrast to text, which was associated systems currently available on the market. There may be no with higher rated most suitable AAC systems. Ideographs available system matching a given description, or there may were not a feature of the most suitable AAC system for any be several different models all having features matching the child vignette. An idiosyncratic layout was a feature of the description; thus, for example, if participants rated 50% of most suitable AAC system for all child vignettes. AAC systems in this survey at least 5 out of 7 for suitability, it does not mean they would give 50% of currently available AAC systems a similar rating. It follows that the proportion Discussion of AAC systems suitable for a given child vignette reported here may not reflect the range of suitable systems that AAC The results show that participants rated the suitability of professionals would consider choosing between in daily prac- AAC systems differently depending on the characteristics of the child vignette they were presented with. There was con- tice. Yet, despite this caveat, the AAC systems presented siderable variation in the fraction of AAC systems that were were considered to be feasible, whether or not they were highly rated, and the features of the most suitable AAC available “off the shelf”, so the results of the current study AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 7 Figure 3. Ratings of the most suitable AAC system for each child vignette. Figure 4. Number of times each AAC system level was part of a child vignette’s most preferred system. do give an indication of the relative numbers of possible excellent, then for all child vignettes at least half of AAC sys- AAC systems that were regarded as acceptable or good for tems were a good fit (the reader may instead choose to different children. interpret 5 as an acceptable rating and 6 as good, for If an average rating of 5 of more out of 7 is taken as example, but the meaning of our discussion is unchanged); good in terms of suitability, and 6 out of 7 taken as however, there was still much variation in the number of 8 E. J. D. WEBB ET AL. AAC systems that were a good fit, from a low of 51.1% to a described as more motivated to communicate via AAC and high of 98.5%. In addition, more variation is revealed in with stronger prognoses for improvement, that implies that terms of excellent systems. Most child vignettes had no AAC there is at least the potential for inequalities in AAC provi- systems that were an excellent fit, yet for one child vignette sion to arise. For some children, fewer AAC systems are well (which could in some ways be considered to have the stron- suited to them, so that barriers to accessing some systems, gest prognosis for improvement) 20.4% of AAC systems were such as cost or requiring a large amount of AAC practitioner considered excellent for suitability. For many child vignettes input to set up, may disproportionately affect them, com- almost all AAC systems were a good fit, yet only a small frac- pared to children for whom many AAC systems are suitable. tion were an excellent fit. In light of this finding, it is encouraging that some dedicated One possible interpretation of most systems being rated funding for AAC systems is available, and Webb et al. good for most child vignettes is that there was a weak (2019b) found that UK AAC professionals ascribed low underlying decision-making rationale. This interpretation importance to cost in their decision-making. However, other would also be consistent with only a small number of evidence suggests cost can play a significant role in AAC vignettes having AAC systems rated over 6: in most cases no professionals’ decision-making in other countries (Van stand-out system (and thus decision rationale) emerged. An Niekerk et al., 2018), and future research could usefully alternative explanation for these observations is that partici- address the extent to which this leads to inequalities in AAC pants rated the suitability of a given AAC system in the con- provision. text of the alternative being no system. If they believed Previous evidence has shown a need for lower learning “some communication is better than none”, then this may demands of AAC systems for some children (Light et al., have led to most AAC systems being rated as good. A fur- 2019; Light, Wilkinson, et al., 2019). This need may have led ther interpretation is that the AAC system attributes used in participants to rate only AAC systems with low learning this study are relatively unimportant compared to other demands highly for some children, explaining some of the attributes not included, although the extensive attribute observed variation in the number of systems with high suit- development process weighs against this possibility. One ability ratings. In particular, it is a plausible explanation as to additional reason behind the finding that many AAC systems why child vignettes predicted to regress in skills and abilities were rated good, but relatively few were rated excellent, is had fewer AAC systems with high suitability ratings. participants’ interpretation of a “suitable match” for a child In line with the previous observation, graphic representa- vignette. It may be that participants interpreted suitable, tion using photos, considered to have lower learning especially on the upper half of the Likert scale, as meaning demands, were commonly a feature of the most suitable adequate. This could have led to participants rating many AAC system for child vignettes predicted to regress in skills AAC systems similarly, rather than making distinctions and abilities and without motivation to communicate using between an adequate and optimal match. If participants did AAC. Photos were associated with having a lower rated most approach the Likert scale tasks in that way, it would lead to suitable AAC system, and text, with greater learning problems in interpreting the study’s results as giving cardinal demands, was associated with having a higher rated most information about strength of preference. However, examin- suitable AAC system. The implication is that for children who ing participants’ responses reveals that they are consistent require an AAC system with low learning demands, not only with them distinguishing between, say, 5 being an adequate were there fewer systems that were a good match, even the match and 7 being an optimal match of AAC system to a most suitable systems were not an ideal match. child vignette. For child vignettes which may broadly be Another factor in how many AAC systems were given described as more motivated to communicate via AAC, and high suitability ratings was whether a child vignette included with a strong prognosis for improvement, participants “was motivated to communicate using AAC” or not. For tended to rate most AAC systems as at least good, but vignettes in which the child was motivated to communicate clearly distinguished some systems as being a better match using AAC, many more AAC systems tended to be rated as for their needs, indicating participants saw a difference good or excellent for suitability. Such motivation was also an between adequate and optimal matching. In addition, suit- important factor in the DCE results, where it led participants ability ratings of AAC systems with greater learning demands to make what could be regarded as more ambitious choices, (large vocabulary, semantic-syntactic organization, etc.) for for example a large vocabulary, or graphic representation more challenging child vignettes were generally lower than using ideographic symbols rather than photos. The current the suitability ratings of basic AAC systems for children moti- vated to use AAC and with strong prognoses for improve- study has given context to this finding by suggesting that, ment. This later observation is consistent with participants although motivation to communicate using AAC was an distinguishing between poor and adequate matches for child important determinant of participants’ choices, the conse- vignettes. quences of such choices were not necessarily large, as partic- ipants regarded many less preferred AAC systems as well suited for motivated children. These findings are in line with Motivation to communicate using AAC and prognosis previous evidence that attitudes toward AAC, and valuing an AAC system are important factors in successfully adopting The vignettes where there were AAC systems ranked over 6 could broadly be described as those where the child was AAC (Johnson et al., 2006; Light & McNaughton, 2014), so AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 9 that children motivated to use AAC are still likely to succeed, an alternative more highly. An alternative explanation is even with an AAC system that is not a perfect match. that in the context of the survey, any degree of layout cus- tomization was classed as idiosyncratic, whereas in practice most AAC professionals wouldn’t necessarily think of them Most suitable AAC systems that way. In any case, this unusual finding should be investi- gated further. The analysis also reveals which AAC systems participants regarded as most suitable for each child vignette. A caveat is that, although combinations of attributes which were Comparison with other findings regarded as unrealistic were excluded by the research team, there is no guarantee that for each AAC system included in Although DCEs are common in healthcare (Soekhai et al., 2019), this is the first study we are aware of that combines the current study, an AAC system exists in the real world choices with ratings. An advantage of this approach is that it that has similar characteristics. There were significant differ- ences between child vignettes in terms of how highly rated gives more information and makes it possible to answer the most suitable AAC systems were, up to 14.3% of the rat- other research questions than with a standard DCE with low ing scale’s available range. This may reflect that, for some extra participant burden and minimal additional resources to children the best available AAC system was not as suitable gather the data. The current study’s novel methods gave to their needs and abilities as for other children. However, quantitative insight into current practice around UK AAC pro- fessionals’ decision-making. Particularly, it and the two linked previous findings have shown that personalizing an off-the- survey studies address the concept of feature matching, i.e., shelf AAC system is an important factor in whether a child matching the characteristics of the child with the most rele- successfully adopts it (Dietz et al., 2012; King et al., 2008; vant AAC system attributes. Their findings can be linked to Light & McNaughton, 2013). Thus it may be that participants evidence from the wider project: Participants included in would have given similar ratings to the most suitable AAC system for all child vignettes if it was clear that they would other elements of the I-ASC study (e.g., Lynch et al., 2019; be personalized to the individual child. Murray et al., 2019) described how they considered and Figure 4 shows the number of times each AAC system made tradeoffs across the decision-making process. At a fea- ture matching level this included consideration of particular level was part of a child vignette’s most preferred system, child characteristics, for example the child’s motivation to and certain characteristics appeared much more often than communicate, their abilities to learn or their likely decline in others. For example, very few child vignettes had a highest learning capacity. Regarding AAC system attributes, we rated AAC system with fewer than 50 vocabulary items. This found consideration of the child’s physical and cognitive is in line with findings from the DCE, which showed that par- characteristics. This included communication aid size and ticipants were always more likely to choose AAC systems weight, which were important for very small children or for with more than 50 vocabulary items than systems with fewer children who were ambulatory. Communication aid appear- than 50, regardless of the child vignette they were choosing ance, voice quality, and reliability were also salient features. for. The result is also consistent with few child vignettes hav- The software attributes prioritized reflected both the needs ing no pre-provided vocabulary sets and many having staged of the child and those providing support. progression as a feature of their most suitable AAC system. A roughly even number of child vignettes had 50–1000 and over 1000 words as part of their most suitable AAC system, Implications so it was not the case that participants believed that more vocabulary items were always better. The different components of research resulted in a general Visual scene was the most preferred mode of vocabulary explanatory model of decision-making in AAC for children. organization for few child vignettes, yet visual scene displays Full details are given in Murray et al. (2020), and a schematic are increasingly common (Beukelman et al., 2021; Wilkinson representation is given in Figure 5. The I-ASC explanatory et al., 2012). This is not necessarily a contradiction, as the model will aid and inform practice by providing a conceptual child vignettes used in this study are intentionally not repre- overview of the linked aspects of decision-making and the sentative of the population of children who would benefit different components of AAC provision for children. It will from AAC. Rather, they represent a wide range of characteris- also stimulate future research by highlighting areas where tics. Thus, it may be that AAC professionals encounter many we currently lack understanding and empirical evidence. children with characteristics similar to the vignettes where In summary, the I-ASC exploration concluded that those visual scene is most suitable, and fewer children similar to charged with the responsibility for proposing specific com- other vignettes. munication aids face a complex task that includes identifying All most preferred systems featured an idiosyncratic lay- the particular child characteristics, access features, and com- out. This uniformity of opinion is surprising. One possible munication aid attributes. A key lesson for practice is that explanation is a form of measurement error. The statistical these must be considered in the recommendations for each model had a limited number of parameters to avoid overfit- child. The challenge is that these are not separate, fixed ting, and it may be that this resulted in some child components of the decision-making process, but are con- vignettes appearing to have idiosyncratic as their most suit- stantly moving, with some being more fluid and others more able layout, whereas in fact participants would have rated stable depending on context as teams reach their decisions. 10 E. J. D. WEBB ET AL. Figure 5. The I-ASC explanatory model of decision making. From Murray et al. (2019). Limitations and future directions the previous choice task; however, incorporating the Likert scale as part of a DCE had many practical advantages, as dis- It is possible to calculate a numerical rating for each AAC cussed above. In addition, recruiting participants to both the system and to test whether any differences are statistically BWS and DCE surveys was difficult given the low numbers of significant. It is not possible, however, to know how mean- AAC professionals in the UK, estimated at around 800 ingful participants considered to be the difference between, (Communication Matters, private correspondence). Thus it for example, an AAC system rated 5 out of 7 and one rated was uncertain whether recruiting participants for a third sur- 6 out of 7. It may be that they considered two such AAC sys- vey would be practical. tems to be very similar, or they could have believed that the In common with all stated preference studies, there is higher rated system would have a significantly positive effect concern about the ecological validity of the survey instru- on a child’s future for many years. Future studies using a ment and whether it captured the relevant aspects of the similar method may wish to investigate giving participants decision-making situation. However, the survey went through guidance as to how to interpret a unit difference in the rat- an extensive development process (Webb et al., 2021)to ing scale. help create as realistic a scenario as possible, and which cap- The ratings for AAC systems are derived from statistical tured the most important aspects of decision-making. A modeling of a limited number of choices for each individual, related limitation is that we could include only a subset of and so participants may have given different responses if the the many characteristics that influence how suitable a match context was changed to rating an AAC system directly; how- an AAC system is for a child. Thus, for example, while we ever, with 36 child vignettes and 274 AAC systems, rating provide information on systematic organization and repre- every system for every vignette would have required partici- sentation, we did not include features related to access. In pants to complete 864 rating tasks, which is unfeasible. In addition, the survey measures what AAC system attributes addition, DCE choices and ratings were gathered at the same participants believed would match well with children. It does time and participants’ ratings may have been influenced by not necessarily capture the effectiveness of the sort of AAC AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 11 systems suggested here to be a suitable match for children, Conclusion and empirical research into whether AAC professionals’ This study complements the earlier BWS and DCE studies, beliefs match up with real-world effectiveness would be use- with all three studies examining the decision-making of AAC ful to investigate in future. professionals choosing AAC systems for children from a dif- It was previously mentioned that the set of AAC systems ferent perspective. There have also been synergies from per- used in the survey was not necessarily reflective of AAC sys- forming the studies together in a single research project. The tems available on the market. Although all systems were results, together with the findings of the wider research pro- feasible, many would not be available off the shelf, and in ject have been used to help create practical resources to practice would require practitioners to adapt an existing AAC help AAC professionals working with children in their every- vocabulary set. The skills, willingness and culture of doing day practice. The suite of resources is freely available at this is likely to vary across practice settings. Additionally the https://iasc.mmu.ac.uk/. availability of AAC systems will vary from place to place (par- ticularly across countries) and the AAC systems and vocabu- laries placed on the market will change over time. Disclosure statement This study had a relatively low sample size compared to No potential conflict of interest was reported by the authors. many similar studies in healthcare (Soekhai et al., 2019). As noted previously, however, the number of AAC professionals Funding in the UK is small, so that the sample size represents a size- able fraction of the target population. This project was funded by the NIHR Health Services and Delivery There was probably some heterogeneity in participants’ Program [project 14/70/153]. The views expressed are those of the opinions and preferences depending on their individual authors and not necessarily those of the NHS, the NIHR or the Department of Health. Stephane Hess acknowledges additional support experiences and familiarity with different AAC systems and by the European Research Council through the consolidator grant children. It is a limitation of our study that we did not collect 615596-DECISIONS. data on what AAC system participants’ were familiar with, and only broad information about what diagnoses they com- ORCID monly encountered; however, it is difficult to collect such detailed information in a short web survey where the focus Edward J. D. Webb http://orcid.org/0000-0001-7918-839X was on preference elicitation tasks. There may also be deci- Yvonne Lynch http://orcid.org/0000-0003-3209-3099 sion-making differences between more experienced AAC pro- Simon Judge http://orcid.org/0000-0001-5119-8094 Juliet Goldbart http://orcid.org/0000-0003-1290-7833 fessionals, as in Sauerwein and Wegner (2020) (though note Liz Moulam http://orcid.org/0000-0003-3810-1037 that in that study, novices were defined as first-year masters Stephane Hess http://orcid.org/0000-0002-3650-2518 students, some of which had not taken an AAC course, Janice Murray http://orcid.org/0000-0001-8809-4256 whereas here 93% of participants had at least one year of professional AAC experience). Future work could examine References heterogeneity in AAC professionals’ decision-making more closely. This includes how decision-making varies among dif- Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10. ferent specialties, which is not possible to explore in the cur- 1109/TAC.1974.1100705 rent study due to 75% of participants having a speech and Beukelman, D., & Light, J. (2020). Augmentative and alternative communi- language therapy background. For example, speech and lan- cation for children and adults. (5th ed.). Paul H. Brookes Publishing Co. guage therapists may have interpreted regression as a loss Beukelman, D. R., Thiessen, A., & Fager, S. K. (2021). Personalization of of language skills, whereas occupational therapists inter- visual scene displays: Preliminary investigations of adults with apha- sia, typical females across the age-span, and young adult males and preted it as a loss of physical abilities or vision, and made females. Topics in Language Disorders, 41(3), e1–E11. https://doi.org/ different decision accordingly. 10.1097/TLD.0000000000000256 There is much scope for future research to build on the Cheung, K. L., Wijnen, B. F., Hollin, I. L., Janssen, E. M., Bridges, J. F., current study. For example, it would be useful to examine Evers, S. M., & Hiligsmann, M. (2016). Using best–worst scaling to whether the findings about the suitability of hypothetical investigate preferences in health care. PharmacoEconomics, 34(12), 1195–1209. https://doi.org/10.1007/s40273-016-0429-5 AAC systems concur with AAC professionals’ opinions about Choi, B. C., & Pak, A. W. (2006). Multidisciplinarity, interdisciplinarity and the suitability of real life systems. In addition, the current transdisciplinarity in health research, services, education and policy: 1. study highlighted areas in which inequalities in provision Definitions, objectives, and evidence of effectiveness. Clinical and could occur, and in future, it could be examined whether Investigative Medicine. Medecine Clinique et Experimentale, 29(6), 351– such inequalities are found. Finally, it would be fruitful to Dietz, A., Quach, W., Lund, S. K., & Mckelvey, M. (2012). AAC assessment explore whether AAC professionals’ opinions about the suit- and clinical-decision making: The impact of experience. Augmentative ability of AAC systems are in agreement with other stake- and Alternative Communication (Baltimore, Md. : 1985), 28(3), 148–159. holders such as people who use AAC and their families. This https://doi.org/10.3109/07434618.2012.704521 latter issue is of particular importance given the likely impact Enderby, P., Judge, S., Creer, S., & John, A. (2013). Examining the need on how motivated a child is to use an AAC system, and on for and provision of AAC methods in the UK. Advances in Clinical how motivated a family is to provide support. Neuroscience & Rehabilitation, 13,20–23. 12 E. J. D. WEBB ET AL. Fried-Oken, M., Mooney, A., & Kinsella, M. (2019). Cognitive Demands Light, J., McNaughton, D., & Caron, J. (2019). New and emerging AAC Checklist for Augmentative and Alternative Communication technology supports for children with complex communication needs (CDC4AAC). Presentation at the Assistive Technology Industry and their communication partners: State of the science and future Association (ATIA) Annual Conference, Orlando, FL. research directions. Augmentative and Alternative Communication Geytenbeek, J. J., Vermeulen, R. J., Becher, J. G., & Oostrom, K. J. (2015). (Baltimore, Md. : 1985), 35(1), 26–41. https://doi.org/10.1080/07434618. Comprehension of spoken language in non-speaking children with 2018.1557251 severe cerebral palsy: An explorative study on associations with Light, J., Wilkinson, K. M., Thiessen, A., Beukelman, D. R., & Fager, S. K. motor type and disabilities. Developmental Medicine and Child (2019). Designing effective AAC displays for individuals with develop- Neurology, 57(3), 294–300. https://doi.org/10.1111/dmcn.12619 mental or acquired disabilities: State of the science and future Gross, J. (2010). Augmentative and alternative communication: A report research directions. Augmentative and Alternative Communication on provision for children and young people in England. Office of the (Baltimore, Md. : 1985), 35(1), 42–55. https://doi.org/10.1080/07434618. Communication Champion. 2018.1558283 Hajjar, D. J., Mccarthy, J. W., Benigno, J. P., & Chabot, J. (2016). You get Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: more than you give”: Experiences of community partners in facilitat- Analysis and applications., Cambridge University Press. ing active recreation with individuals who have complex communica- Lund, S. K., & Light, J. (2006). Long-term outcomes for individuals who tion needs. Augmentative and Alternative Communication (Baltimore, use augmentative and alternative communication: Part I–What is a Md. : 1985), 32(2), 131–142. https://doi.org/10.3109/07434618.2015. “good. Augmentative and Alternative Communication (Baltimore, Md. : 1136686 1985), 22(4), 284–299. https://doi.org/10.1080/07434610600718693 Hemsley, B., & Murray, J. (2015). Distance and proximity: Research on Lund, S. K., Quach, W., Weissling, K., Mckelvey, M., & Dietz, A. J. (2017). social media connections in the field of communication disability. Assessment with children who need augmentative and alternative Disability and Rehabilitation, 37(17), 1509–1510. https://doi.org/10. communication (AAC): Clinical decisions of AAC specialists. Language, 3109/09638288.2015.1057031 Speech, and Hearing Services in Schools, 48(1), 56–68. https://doi.org/ Hess, S., & Palma, D. (2019). Apollo: A flexible, powerful and customis- 10.1044/2016_LSHSS-15-0086 able freeware package for choice model estimation and application. Lynch, Y., Murray, J., Moulam, L., Meredith, S., Goldbart, J., Smith, M., Journal of Choice Modelling, 32, 100170. https://doi.org/10.1016/j.jocm. Batorowicz, B., Randall, N., & Judge, S. (2019). Decision-making in 2019.100170 communication aid recommendations in the UK: Cultural and context- Hynan, A., Goldbart, J., & Murray, J. (2015). A grounded theory of inter- ual influencers. Augmentative and Alternative Communication net and social media use by young people who use augmentative (Baltimore, Md. : 1985), 35(3), 180–192. https://doi.org/10.1080/ and alternative communication (AAC). Disability and Rehabilitation, 07434618.2019.1599066 37(17), 1559–1575. https://doi.org/10.3109/09638288.2015.1056387 McFadd, E., & Wilkinson, K. (2010). Qualitative analysis of decision mak- Johnson, J. M., Inglebret, E., Jones, C., & Ray, J. (2006). Perspectives of ing by speech-language pathologists in the design of aided visual dis- speech language pathologists regarding success versus abandonment plays. Augmentative and Alternative Communication, 26(2), 136–147. of AAC. Augmentative and Alternative Communication, 22(2), 85–99. https://doi.org/10.3109/07434618.2010.481089 https://doi.org/10.1080/07434610500483588 Murray, J., Lynch, Y., Goldbart, J., Moulam, L., Judge, S., Webb, E., Jayes, Judge, S., Enderby, P., Creer, S., & John, A. (2017). Provision of powered M., Meredith, S., Whittle, H., Randall, N., Meads, D., & Hess, S. (2020). communication aids in the United Kingdom. Augmentative and The decision-making process in recommending electronic communi- Alternative Communication (Baltimore, Md. : 1985), 33(3), 181–187. cation aids for children and young people who are non-speaking: The https://doi.org/10.1080/07434618.2017.1347960 I-ASC mixed-methods study. Health Services and Delivery Research, Judge, S., Randall, N., Goldbart, J., Lynch, Y., Moulam, L., Meredith, S., & 8(45), 1–158. https://doi.org/10.3310/hsdr08450 Murray, J. (2020). The language and communication attributes of Murray, J., Lynch, Y., Meredith, S., Moulam, L., Goldbart, J., Smith, M., graphic symbol communication aids–a systematic review and narra- Randall, N., & Judge, S. (2019). Professionals’ decision-making in rec- tive synthesis. Disability and Rehabilitation. Assistive Technology, 15(6), ommending communication aids in the UK: Competing considera- 652–662. https://doi.org/10.1080/17483107.2019.1604828 tions. Augmentative and Alternative Communication, 35(3), 167–179. King, G., Batorowicz, B., & Shepherd, T. A. (2008). Expertise in research- https://doi.org/10.1080/07434618.2019.1597384 informed clinical decision making: Working effectively with families of NHS England. (2016). Guidance for commissioning AAC services and children with little or no functional speech. Evidence-Based equipment. https://www.england.nhs.uk/commissioning/wp-content/ Communication Assessment and Intervention, 2(2), 106–116. https://doi. uploads/sites/12/2016/03/guid-comms-aac.pdf org/10.1080/17489530802296897 Royal College of Speech and Language Therapists (2009). Resource man- Kuhfeld, W. F., Tobias, R. D., & Garratt, M. J. (1994). Efficient experimental ual for commissioning and planning services for SLCN. Royal College of design with marketing research applications. Journal of Marketing Speech and Language Therapists. Research, 31(4), 545–557. https://doi.org/10.1177/0022243794031 Ryan, S. E., Shepherd, T., Renzoni, A. M., Anderson, C., Barber, M., 00408 Kingsnorth, S., & Ward, K. (2015). Towards advancing knowledge Light, J., & McNaughton, D. (2013). Putting people first: Re-thinking the translation of AAC outcomes research for children and youth with role of technology in augmentative and alternative communication complex communication needs. Augmentative and Alternative intervention. Augmentative and Alternative Communication (Baltimore, Communication, 31(2), 137–147. https://doi.org/10.3109/07434618. Md. : 1985), 29(4), 299–309. https://doi.org/10.3109/07434618.2013. 2015.1030038 848935 Sauerwein, A. M., & Wegner, J. R. (2020). Clinical reasoning skills in AAC Light, J., & McNaughton, D. (2014). Communicative competence for indi- intervention planning: Investigating the expert-novice gap. Teaching viduals who require augmentative and alternative communication: A and Learning in Communication Sciences & Disorders, 4(2), 7. https:// new definition for a new era of communication? Augmentative and doi.org/10.30707/TLCSD4.2/XNDO8764 Alternative Communication (Baltimore, Md. : 1985), 30(1), 1–18. https:// Schlosser, R. W., & Wendt, O. (2008). Effects of augmentative and alterna- doi.org/10.3109/07434618.2014.885080 tive communication intervention on speech production in children Light, J., McNaughton, D., Beukelman, D., Fager, S. K., Fried-Oken, M., with autism: A systematic review. American Journal of Speech- Jakobs, T., & Jakobs, E. (2019). Challenges and opportunities in aug- Language Pathology, 17(3), 212–230. https://doi.org/10.1044/1058- mentative and alternative communication: Research and technology 0360(2008/021) development to enhance communication and participation for indi- Soekhai, V., De Bekker-Grob, E. W., Ellis, A. R., & Vass, C. M. (2019). viduals with complex communication needs. Augmentative and Discrete choice experiments in health economics: Past, present and Alternative Communication (Baltimore, Md. : 1985), 35(1), 1–12. https:// future. PharmacoEconomics, 37(2), 201–226. https://doi.org/10.1007/ doi.org/10.1080/07434618.2018.1556732 s40273-018-0734-2 AUGMENTATIVE AND ALTERNATIVE COMMUNICATION 13 Sundqvist, A., & Ronnberg, € J. (2010). A qualitative analysis of email inter- Webb, E. J., Meads, D., Lynch, Y., Judge, S., Randall, N., Goldbart, J., Meredith, S., Moulam, L., Hess, S., & Murray, J. (2021). Attribute selec- actions of children who use augmentative and alternative communi- tion for a discrete choice experiment incorporating a best-worst scal- cation. Augmentative and Alternative Communication (Baltimore, Md. : ing survey. Value in Health : The Journal of the International Society for 1985), 26(4), 255–266. https://doi.org/10.3109/07434618.2010.528796 Pharmacoeconomics and Outcomes Research, 24(4), 575–584. https:// Thistle, J. J., & Wilkinson, K. M. (2015). Building evidence-based practice doi.org/10.1016/j.jval.2020.10.025 in AAC display design for young children: Current practices and Webb, E. J., Meads, D., Lynch, Y., Randall, N., Judge, S., Goldbart, J., future directions. Augmentative and Alternative Communication Meredith, S., Moulam, L., Hess, S., & Murray, J. (2019b). What’s import- (Baltimore, Md. : 1985), 31(2), 124–136. https://doi.org/10.3109/ ant in AAC decision making for children? Evidence from a best–worst 07434618.2015.1035798 scaling survey. Augmentative and Alternative Communication Van Niekerk, K., Dada, S., Tonsing, € K., & Boshoff, K. (2018). Factors per- (Baltimore, Md. : 1985), 35(2), 80–94. https://doi.org/10.1080/07434618. ceived by rehabilitation professionals to influence the provision of 2018.1561750 assistive technology to children: A systematic review. Physical & Wilkinson, K. M., Light, J., & Drager, K. (2012). Considerations for the Occupational Therapy in Pediatrics, 38(2), 168–189. https://doi.org/10. composition of visual scene displays: Potential contributions of infor- 1080/01942638.2017.1337661 mation from visual and cognitive sciences. Augmentative and Webb, E. J., & Hess, S. (2021). Joint modelling of choice and rating data: Alternative Communication (Baltimore, Md. : 1985), 28(3), 137–147. Theory and examples. Journal of Choice Modelling, 40, 100304. https:// https://doi.org/10.3109/07434618.2012.704522 doi.org/10.1016/j.jocm.2021.100304 Williams, M., Beukelman, D., & Ullman, C. (2012). AAC text messaging. Webb, E. J., Lynch, Y., Meads, D., Judge, S., Randall, N., Goldbart, J., Perspectives on Augmentative and Alternative Communication, 21(2), Meredith, S., Moulam, L., Hess, S., & Murray, J. (2019a). Finding the 56–59. https://doi.org/10.1044/aac21.2.56 best fit: Examining the decision-making of augmentative and alterna- Williams, M. B., Krezman, C., & McNaughton, D. J. (2008). Reach for the tive communication professionals in the UK using a discrete choice stars”: Five principles for the next 25 years of AAC. Augmentative and experiment. BMJ Open, 9(11), e030274. https://doi.org/10.1136/ Alternative Communication, 24(3), 194–206. https://doi.org/10.1080/ bmjopen-2019-030274 08990220802387851

Journal

Augmentative and Alternative CommunicationTaylor & Francis

Published: Jul 3, 2023

Keywords: Children; clinical decision-making; discrete choice experiment; likert scale; stated preferences

There are no references for this article.