Parents’ attitudes towards using assistive technologies for children with ASD in Jordan
Bibliographic record
Abstract
This study aimed to evaluate the acceptance and attitudes of Jordanian parents toward assistive technology (AT) for children with autism spectrum disorder (ASD) using the Unified Theory of Acceptance and Use of Technology (UTAUT). In this cross-sectional study, 130 parents participated, most female (73.8%) and over 34 (70.8%). The majority (89.6%) reported that their children used smartphones, with 68.5% using them several times daily. Smartphones (89.6%) and iPads (24%) were the most frequently used technologies, while talking books (4%) and smart boards (2.4%) had the lowest usage. UTAUT results showed moderate agreement in most factors: effort expectancy (68.7%), performance expectancy (58.7%), and attitudes toward technology (65%). Notably, 47.8% of parents reported low social support for using AT, likely due to limited awareness and financial constraints. Regression analysis revealed that technology usage explained 41% of the variance in performance expectancy, while parental factors accounted for 43% of the variance in effort expectancy. Significant positive relationships were found between AT usage, behavioral intention, and actual use. These findings suggest that increasing technology usage and social support may enhance the adoption of AT for children with ASD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".