Experience of Autistic Children and Their Families During the Pandemic: From Distress to Coping Strategies
Bibliographic record
Abstract
Abstract Background. As the COVID-19 pandemic unfolded during Spring 2020, families experienced multiple upheavals, including lockdown, school closures and ruptures in professional services. We wanted to better understand the experience of autistic children and their families in times of crisis. Methods. 109 parents of autistic children (2.6–18 years) and 56 autistic children (5.75–18 years) from Quebec (Canada) completed an online survey about needs, barriers and facilitators to coping with the pandemic. Quantitative data were analyzed using ANOVA, chi-squares and open-ended questions with thematic analysis. Results. Half of the parents and children considered the pandemic to be a stressful time. Parents who expressed concerns about their child’s development and difficulty managing their child’s behaviors during the pandemic were significantly more likely than other parents to report these concerns before the pandemic, along with the following challenges during the pandemic: social isolation, having to pursue academic goals, feeling powerless over their child’s behaviors and no routine during the pandemic (all p < 0.05). Maintaining social relationships and implementing appropriate strategies, such as a routine, were identified as facilitators by both parents and children. Among children, 92.9% associated technologies with their well-being, but many parents saw too much access to electronics by their child as a barrier, suggesting a need to better understand and use autistic interests. Conclusion. Both autistic children and their parents identified social isolation as one of the main difficulties during the pandemic. We also need to consider autistic characteristics and children’s interests to implement emergency accommodations and services.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".