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

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

Notice bibliographique

RevueThe Medicine Forum · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueAssistive Technology in Communication and Mobility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSample (material)Quality of life (healthcare)Intellectual disabilitySpecial educationService providerDescriptive statisticsQuality (philosophy)Assistive technologyInclusion (mineral)Context (archaeology)
DOInon disponible

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,027

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,005
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,072
Tête enseignante GPT0,488
Écart entre enseignants0,416 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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