THE CHALLENGES OF CAREGIVERS OF CHILDREN WITH AUTISM SPECTRUM DISORDERS COMORBIDITY DURING THE COVID-19 PANDEMIC IN SERBIA
Bibliographic record
Abstract
BACKGROUND: Children with Autism Spectrum Disorders (ASD) experience significantly higher prevalence of other mental disorders, which amplifies their need for overall support. The outbreak of novel coronavirus (COVID-19) resulted in restrictions and limited access to different services with great challenge for families and children with ASD. SUBJECTS AND METHODS: We used an electronic SurveyMonkey questionnaire to examine the experiences of 114 caregivers of children with ASD. We compared: (a) level of support by the child's school, changes in child behavior, and priority needs for families of ASD and ASD with comorbidities (ASD+) children, during pandemic, and (b) developmental history and diagnosis for ASD and ASD+ children before the pandemic. RESULTS: Our research shows significant behavioral difficulties in the population with ASD and ASD+ that arose in the field of altered living conditions and overall functioning during the COVID-19 pandemic. Statistically significant results comparing ASD to ASD+ children we found in area of getting additional help and support before the outbreak of the pandemic (47.1% vs 16.0%, p=0.002), as well as in worsening of sleep problems, statistically significant more common in children with ASD+ (ASD+ 47.7% vs. ASD 25.7%, p=0.046). CONCLUSIONS: Our findings can contribute to the faster development and implementation of protocols for dealing with situations such as pandemics, related to the vulnerable population of children with ASD and their caregivers.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".