Background: There is an increasing number of technological resources available to speech and language therapists (SLTs) for use in clinical practice, but the factors that influence SLTs' selection and use of such resources are not well understood. In related fields, technology acceptance models have been employed to explain users' adoption of technology and to inform the advancement of empirically supported technological resources. Aims: To determine the factors that influence SLTs' use of technology for clinical practice by testing a model of their technology acceptance and use. Methods & Procedures: We surveyed 209 practising SLTs in the United States representative of the speech and language membership of the American Speech-Language-Hearing Association (ASHA). Participants completed a 38-item electronic survey representing four categories: (1) technology use, (2) technology attitudes and factors influencing technology use, (3) employment information and (4) demographics. Items measuring technology attitudes served as indicators of the research model, which mapped the primary relationships of a technology acceptance model. Survey data were collected before the Covid-19 pandemic.Outcomes & Results: The research model accounted for 66% of the variance in SLTs' behavioural intention to use technology, which significantly and positively predicted the amount of time they reportedly spent using technology in the workplace. Subjective norms and attitudes towards technology use directly predicted the intention to use technology. Perceived usefulness and ease of use indirectly predicted intention to use technology. Survey respondents reported using technology during 48% (SD = 24%) of their overall weekly work hours on average, with a large majority reporting using technology at least once per week for planning (89% of respondents), assessment (66% of respondents) or intervention (90% of respondents).Conclusions & Implications: These findings statistically explain the relationships between SLTs' attitudes and their intention to use technology for clinical practice, contributing to our understanding of why SLTs adopt certain technologies. We also detail the nature and frequency of technology use in the clinical practice of SLTs. Future directions for this work include further exploring use categories, employing direct measurements of technology use and exploring the impact of recent changes in SLT service delivery due to the Covid-19 pandemic on SLTs' technology attitudes.
Purpose: The differential diagnosis of developmental language disorder (DLD) in bilingual children represents a unique challenge due to their distributed language exposure and knowledge. The current evidence indicates that dual-language testing yields the most accurate classification of DLD among bilinguals, but there are limited personnel and resources to support this practice. The purpose of this study was therefore to determine the feasibility of dual-language automatic speech recognition (ASR) for identifying DLD in bilingual children. Method: Eighty-four Spanish–English bilingual second graders with ( n = 25) and without ( n = 59) confirmed diagnoses of DLD completed the Bilingual English-Spanish Assessment–Middle Extension Morphosyntax in both languages. Their responses on a subset of items were scored manually by human examiners and programmatically by a researcher-developed ASR application employing a commercial speech-to-text algorithm. Results: Results demonstrated moderate overall item-by-item scoring agreement ( k = .54) and similar classification accuracy values (human = 92%, ASR = 88%) between the two methods using the best-language score. Classification accuracy of the ASR method increased to 94% of cases correctly classified when test items with poorer discrimination in the ASR condition were eliminated. Conclusion: This study provides preliminary support for the technical feasibility of ASR as a bilingual expressive language assessment tool. Supplemental Material: https://doi.org/10.23641/asha.20249994
Purpose: Semantic tasks evaluate dimensions of children's lexical-semantic knowledge. However, the relative ease of semantic task completion depends on individual differences in developmental and language experience factors. The purpose of this study was to evaluate how language experience and language ability impact semantic task difficulty in English for school-age Spanish–English bilingual children with and without developmental language disorder (DLD). Method: Participants included 232 Spanish–English bilingual children in second through fifth grade with ( n = 35) and without ( n = 197) DLD. Data included children's performance on the English Semantics subtest of the Bilingual English–Spanish Assessment—Middle Extension Field Test Version (BESA-ME), age of English acquisition, and percent English language exposure. Task difficulty, a measurement of the relative ease of task completion, was calculated for six semantic task types included on the BESA-ME. Multilevel regression modeling was conducted to estimate longitudinal growth trajectories for each semantic task type. Results: Results showed that language ability and grade level drive semantic task difficulty for all task types, and children with DLD experienced greater difficulty on all task types compared to their typically developing peers. Longitudinally, semantic task difficulty decreased for all children, regardless of language ability, indicating that semantic task types became easier over time. While children made gains on all semantic tasks, the growth rate of task difficulty was not equal across task types, where some task types showed slower growth compared with others. English language exposure emerged as a significant predictor of semantic task difficulty while age of acquisition was not a significant factor. Conclusions: This study clarifies developmental profiles of lexical-semantic performance in bilingual children with and without DLD and supports clinical decision-making regarding children's English language learning.
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