Factors Perceived by Rehabilitation Professionals to Influence the Provision of Assistive Technology to Children: A Systematic Review
Bibliographic record
Abstract
Background: The use of Assistive Technology (AT) by children with disabilities has been associated with significant development and improvement in outcomes within all spheres of life. However, AT is often underutilized. Appropriate selection of AT by rehabilitation professionals could improve the satisfaction of the user and their family with their AT. Data sources: A systematic search identified six studies that investigate the factors that occupational therapists, physiotherapists, as well as speech and language pathologists perceive to influence their provision of AT to children. Study appraisal: Two qualitative and four quantitative articles were identified. Both article types were appraised using the Mixed Methods Appraisal tool (Pluye et al., 2011 Pluye, P., Robert, E., Cargo, M., & Bartlett, G. (2011). Proposal: A mixed methods appraisal tool for systematic mixed studies reviews. (pp. 1–8). Montréal: McGill University, (Part I), Retrieved from http://mixedmethodsappraisaltoolpublic.pbworks.com/w/file/84371689/MMAT 2011 criteria and tutorial 2011-06-29updated2014.08.21.pdf [Google Scholar]). Synthesis method: A process of deductive thematic analysis by using themes from the Assistive Technology Device Selection Framework (Scherer et al., 2007 Scherer, M., Jutai, J., Fuhrer, M., Demers, L., & Deruyter, F. (2007). A framework for modelling the selection of assistive technology devices (ATDs). Disability and Rehabilitation: Assistive Technology, 2(1), 1–8. Retrieved from http://0-informahealthcare.com.innopac.up.ac.za/doi/abs/10.1080/17483100600845414[Taylor & Francis Online] , [Google Scholar]), was followed by inductive thematic analysis to uncover subthemes. Data from all six articles are synthesized to provide a view of factors that are perceived to influence AT selection. Implications of findings: Within a family-centered perspective, both family and child expectations and preferences should be considered. Professionals should consider the influence of their own preferences and knowledge on the AT they recommend.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".