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Record W4414465479 · doi:10.12968/ijtr.2024.0148

Primary caregivers’ perspectives on the use of assistive technologies in children with physical disabilities: a qualitative systematic review

2025· article· en· W4414465479 on OpenAlexaff
Maedeh Loabichian, Zeinab Salari Zare, Marzieh Pashmdarfard

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2025
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsThematic analysisAssistive technologyCritical appraisalInclusion (mineral)Qualitative researchQuality (philosophy)Systematic reviewQualitative property

Abstract

fetched live from OpenAlex

Background/Aims Assistive technologies are critical for promoting independence and participation in children with physical disabilities. Assistive technology refers to any item, equipment, or product system used to increase, maintain, or improve the functional capabilities of individuals with disabilities. However, the successful use of these tools relies heavily on the role of primary caregivers. This qualitative systematic review was conducted to investigate primary caregivers’ perspectives on the use of assistive technologies in children with physical disabilities. Methods Four databases (PubMed, Scopus, Medline, and Web of Science) were systematically searched from 2002 to 2025. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline was used to report the literature search in different phases, and the Critical Appraisal Skills Programme was used to evaluate the studies that met the inclusion criteria. Buttler's thematic analysis approach for qualitative research was used for data synthesis. Results A total of eight qualitative studies were included. Two analytical themes related to perspectives of primary caregivers on the use of assistive technologies in children with physical disabilities were explored: positive effects of using assistive technologies and barriers and challenges of using assistive technologies. Primary caregivers view assistive technologies as valuable tools for enhancing children's independence, participation, communication and overall quality of life. However, their effective use is often hindered by funding challenges, limited caregiver training, stigma, language barriers, and inconsistent professional and community support. Optimising assistive technology outcomes requires addressing these barriers alongside device provision. Conclusions Assistive technologies are essential tools that can enhance the lives of children with physical disabilities. These technologies can empower them to participate in everyday activities, fostering independence and improving their overall quality of life. Caregivers have the ability to encourage or hinder the use of assistive technologies; therefore exploring caregivers’ perspectives is crucial in ensuring the successful adoption and use of assistive technologies Implications for practice For allied health professionals, these findings highlight the importance of adopting a family-centred approach by involving caregivers in all stages of assistive technology assessment, selection and follow-up. Providing practical, hands-on training and ongoing support can enhance caregivers’ confidence and promote successful device use. Professionals should also address stigma and accessibility by ensuring that assistive technology solutions fit seamlessly into children's daily routines and supporting families in managing social perceptions. Additionally, facilitating access to funding, streamlining service pathways and collaborating closely with schools, community services and other rehabilitation specialists are essential to optimise assistive technology integration and maximise benefits for children and their families.

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)0.038
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.038
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0110.012
Science and technology studies0.0030.002
Scholarly communication0.0030.004
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.434
Teacher spread0.371 · 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 designSystematic review
Domainnot available
GenreReview

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
Has abstractyes

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