Impact of COVID-19 on Educational Services in Canadian Children With Attention-Deficit/Hyperactivity Disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it