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Record W7119248205

Assistive Technology Transitions from School to Adult Life for Students with Intellectual Disability: A Cross-Sectional Survey

2025· article· en· W7119248205 on OpenAlexaboutno aff
Clements, MS, OTR/L, ATP, Madeleine

Bibliographic record

VenueThe Medicine Forum · 2025
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsSample (material)Quality of life (healthcare)Intellectual disabilitySpecial educationService providerDescriptive statisticsQuality (philosophy)Assistive technologyInclusion (mineral)Context (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

IntroductionIn the United States, the Individuals with Disabilities Education Act mandates that schools consider assistive technology (AT) during transition planning for students with intellectual disability (ID) as they move from school to adult life (Individuals with Disabilities Education Act, 2004). AT can improve outcomes for adults with ID across multiple areas, including vocational, daily living, and communication (Johnson et al., 2023; Morash-Macneil et al., 2018). However, AT is often underutilized by adults with ID (Boot et al., 2017; Alshamrani et al., 2025). The Quality Indicators Assistive Technology (QIAT)-Transition framework provides best practice guidelines, in the form of six quality indicators, for facilitating AT transitions into adulthood (QIAT, 2015). However, no research has used these guidelines to explore the quality of AT transitions. ObjectiveThus, this study examined how special education teachers and related service providers perceive the quality, supports, and barriers of AT transitions from school to adult life for students with ID in the United States. MethodsThe study used a descriptive e-survey and collected data from a convenience sample of 143 special education teachers and related service providers. Quantitative survey responses were analyzed using descriptive statistics, and open-ended responses were analyzed using directed content analysis based on the modified version of the Consolidated Framework for Implementation Research (CFIR) (Damschroder et al., 2022). ResultsFor each of the six QIAT-Transition quality indicators, approximately half (40.6%–66.5%) of the respondents reported that their practice was aligned with the stated indicator. Participants reported that their practice was least aligned with quality indicator 4 (43.4%), which refers to identifying AT needs in the adult environment, and indicator 6 (40.6%), which refers to addressing specific equipment, training, and funding issues. Supports and barriers were primarily identified within CFIR’s Inner Setting domain, although they were also represented across all five domains. Key supports included Improving Documentation (Inner Setting domain), Training (Inner Setting domain), and Increasing Family Knowledge (Individual domain). Key barriers were Barriers to Adult Services (Outer Setting domain), Policies & Procedures (Inner Setting domain), Challenges with AT Use (Inner Setting domain), Staff Characteristics (Individual domain), and Challenges with Team Collaboration (Implementation domain). ConclusionResults indicate limited alignment of practice with QIAT-Transition, which may impact AT use as young adults transition from school to adult life. School teams should consider how the identified supports and barriers can guide AT transition planning in their school(s). These findings can inform professional development initiatives and policies aimed at strengthening AT transition planning and ensuring continuity of support for students with ID. References Alshamrani, K. A., Roll, M. C., Taylor, A. A., Sharp, J. L., & Graham, J. E. (2025). Assistive technology services for transition-aged young adults with disabilities in state-federal vocational rehabilitation programs. Disability and Rehabilitation: Assistive Technology, 20(8), 2804–2820. https://doi.org/10.1080/17483107.2025.2532702 Boot, F. H., Dinsmore, J., Khasnabis, C., & MacLachlan, M. (2017). Intellectual disability and assistive technology: opening the GATE wider. Frontiers in Public Health, 5(19), 1-4. https://doi.org/10.3389/fpubh.2017.00010 Damschroder, L.J., Reardon, C.M., Widerquist, M.A.O. (2022). The updated consolidated framework for implementation research based on user feedback. Implementation Science, 17(75), 1-16. https://doi.org/10.1186/s13012-022-01245-0 Individuals with Disabilities Education Improvement Act of 2004, Pub. L. No. 108–446, § 1400 et seq. (2004) Johnson K.R., Blaskowitz, M.G., & Mahoney, W.J. (2023). Technology for adults with intellectual disability: Secondary analysis of a scoping review. Canadian Journal of Occupational Therapy, 90(4), 395-404. https://doi.org/10.1177/00084174231160975 Morash-Macneil, V., Johnson, F., & Ryan, J. B. (2018). A systematic review of assistive technology for individuals with intellectual disability in the workplace. Journal of Special Education Technology, 33(1), 15-26. https://doi.org/10.1177/0162643417729166 Quality Indicators Assistive Technology (QIAT), 2015. Quality indicators assistive technology-transition. https://qiat.org/new/wp-content/uploads/2020/11/QI-6_-Assistive-Technology-Transition.pdf SynopsisSchools are required to consider assistive technology when helping students with intellectual disability transition from school to adult life. However, assistive technology is often underused in adulthood for this population. This study surveyed special education teachers and related service providers to determine how closely their practices aligned with best practice quality indicators. About half reported following best practices. Common challenges included limited adult services, district policies, and difficulty collaborating. Helpful supports included better documentation, staff training, and increased family knowledge. The findings suggest schools can improve planning to help students use AT more successfully in adulthood. AcknowledgmentsMarie-Christine Potvin, PhD, OTR/L; Pamela Talero-Cabrejo, OTD, BSOT(Col), OTR/L, CPAM, COT

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.488
Teacher spread0.416 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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