Implementing a routine outcome assessment procedure to evaluate the quality of assistive technology service delivery for children with physical or multiple disabilities: Perceived effectiveness, social cost, and user satisfaction
Bibliographic record
Abstract
There is a lack of evidence on the effects and quality of assistive technology service delivery (ATSD). This study presents a quasi-experimental 3-months follow-up using a pre-test/post-test design aimed at evaluating outcomes of assistive technology (AT) interventions targeting children with physical and multiple disabilities. A secondary aim was to evaluate the feasibility of the follow-up assessment adopted in this study with a view to implement the procedure in routine clinical practice. Forty-five children aged 3-17 years were included. Parents were asked to complete the Individual Prioritised Problem Assessment (IPPA) for AT effectiveness; KWAZO (Kwaliteit van Zorg [Quality of Care]) and Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) 2.0 for satisfaction with ATSD; Siva Cost Analysis Instrument (SCAI) for estimating the social cost of AT interventions. At follow-up, 25 children used the AT recommended. IPPA effect sizes ranged from 1.4 to 0.7, showing a large effect of AT interventions. Overall, parents were satisfied with ATSD, but Maintenance, Professional Services, and AT Delivery were rated not satisfactory. SCAI showed more resources spent for AT intervention compared to human assistance without technological supports. AT may be an effective intervention for children with disabilities. Issues concerning responsiveness and feasibility of the IPPA and the SCAI instruments are discussed with a view to inform routine clinical practice.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".