Impact of COVID-19 on Educational Services in Canadian Children With Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
The COVID-19 pandemic led to school closures and a rapid transition to online classes. However, little is known about the impact of online learning in Canadian children with Attention-Deficit/Hyperactivity Disorder (ADHD). An online survey created on Qualtrics was distributed to families across Canada. Data collection was conducted over a total of five weeks in May and June 2020. We reviewed 587 surveys (4% margin of error using a 95% confidence interval) completed by caregivers/parents of children with ADHD (mean child age 10.14 years, SD = 3.06). Survey questions focused on hours of schoolwork completed and whether the learning needs of children with ADHD were met during school closures. Results indicated 90% of children with ADHD received web-based learning during the pandemic. Parents (41%) reported < 5 h of schoolwork per week, and 35% indicated between 5 to 10 h. Of the parents who said their child with ADHD had a modified curriculum (68%), 40% reported receiving educational materials that met their learning expectations during online classes. Parents (59%) reported that their child found it “very challenging” adjusting to online classes. The results indicated that children with ADHD faced significant challenges in adapting to online learning during the pandemic. Binary logistic regression indicated significant associations between depression severity, difficulties with starting and managing tasks and challenges adjusting to online learning. Long-term consequences of these challenges will need to be determined to ensure children with ADHD are able to meet their academic expectations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".