Telehealth Services for Children With Autism Spectrum Disorders in Rural Areas of the Kingdom of Saudi Arabia: Overview and Recommendations
Bibliographic record
Abstract
Autism spectrum disorders (ASD) are the most-prevalent neurodevelopmental disorders. However, each child diagnosed with ASD presents with a unique range of behavioral and communication problems and issues with social skills. Many studies have highlighted the importance of early interventions for children with ASD to improve their skills and provide their families with the necessary support. However, in the Kingdom of Saudi Arabia (KSA), the earliest that a child with ASD in the major cities receives an intervention is at the age of 4 years, owing to limited services and a lack of awareness of the importance and benefits of early interventions. Families who live in rural areas of KSA arguably have a greater need for these services, as they have to travel to cities such as Riyadh for help. The use of telehealth services may be effective for ASD intervention among children living in rural areas, since such services use technology to provide consultations, interventions, diagnosis, training, and education. Research indicates that telehealth services are as valuable as traditional face-to-face treatment, allow families to obtain support from their homes, and help them improve their quality of life. This review will discuss the application of telehealth services to support families in rural areas of KSA who are dealing with issues of ASD, considering the cultural and religious contexts. In addition, it will examine ways in which technology can be employed to suit KSA's culture and needs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".