Investigating Challenges of Individuals with Autism Spectrum Disorder During and Post COVID-19, and Practical Suggestions for Parents and Caregivers on How to Tackle Challenges: A Systematic Review Article
Bibliographic record
Abstract
Introduction: The first and second waves of COVID-19 were unprecedented situations that caused turmoil in everyone’s life, especially for individuals with autism spectrum disorder (ASD) who were severely affected by the transition. Objective: This article investigates the major impacts of COVID-19 transitioning on individuals in the spectrum followed by recommendations for autistic families on addressing concerns caused by post-COVID adaptations. We have based our study on the most recent investigations of challenges and solutions provided for these individuals in COVID and post-COVID-19 transitions. We will also explain telehealth as the most practical available solution and describe its possible advantages and disadvantages. The articles have been selected from PubMed, Google Scholar, etc. according to the goals stated above. Results: According to our study, psychological problems, economic problems, reduced physical activity, sleep disorders, malnutrition, non-cooperation of children with ASD, and speech therapy were the most important challenges for people with ASD as well as their families and caregivers during the COVID-19 pandemic. Conclusion: It was concluded the use of telehealth could be a suitable platform for many educational methods such as applied behavior analysis, visual programs, speech therapy, family positive reinforcement systems, etc. to overcome these challenges. However, this method also has certain limitations, and therefore a combination of face-to-face and telehealth methods is recommended to help patients.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".