Caregiver perspectives on the continued impact of the COVID-19 pandemic on children with intellectual/developmental disabilities
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted caregivers, especially those raising a child with an intellectual/developmental disability (IDD). While research has shown substantial disruption to the family, school, and occupational lives of the IDD community, little is known about the long-term impacts of COVID-19. To address this question, 249 caregivers were surveyed via an online questionnaire, between April and August of 2022 (more than 2 years into the pandemic) about potential impacts of the COVID-19 pandemic on their child's access to health- and school-based therapeutic services, caregiver mental health, and family life. The majority of caregivers reported disruptions in access to and quality of school-based therapeutic services for their child as well as a reduction in educational accommodations in the 2021-2022 academic year. Nearly half of caregivers reported feeling anxious and almost a quarter reported feeling depressed for the majority of their days. More than half of respondents reported decreased social support, and one-fifth reported employment disruptions and decreased access to food. These findings suggest that families of children with IDD are still experiencing ongoing negative impacts of the pandemic, emphasizing the critical need for continued support in the wake of the initial and more obvious disruptions caused by the COVID-19 outbreak.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".