Investigating the efficacy of tele-occupational therapy for ADHD children during COVID-19: a clinical sample in Qatar
Bibliographic record
Abstract
Abstract Objectives To study the effectiveness of tele-occupational therapy (tele-OT) interventions for children with ADHD during the COVID-19 pandemic as well as to assess parental satisfaction with tele-occupational therapy for their children. Methods This is an open prospective cohort study using a clinical convenience sample of twenty children aged 7–15 years old. All children have a known diagnosis of ADHD and are following occupational therapy services at the Child & Adolescent Mental Health Service (CAMHS) outpatient clinic at Hamad Medical Corporation (HMC) in Doha (Qatar). They underwent tele-occupational therapy two sessions per week for 3 months via telephone and/or virtual sessions. The NICHQ Vanderbilt Assessment Scale was used to assess ADHD symptoms and performance pre- and post-intervention. Parents completed a satisfaction survey at the end of the intervention. Results A significant reduction in total ADHD symptoms was observed post-therapy (Z = − 3.932, p < 0.001), with mean scores decreasing from 29.45 (SD = 6.747) to 24.70 (SD = 5.814). There also was a statistically significant difference between pre-tele-occupational therapy and post-tele-occupational therapy average performance scores, decreasing from 2.75 (SD = 0.492) to 2.62 (SD = 0.530) (Z = − 2.347, p = 0.019). Most parents were very satisfied with their experience of the tele-OT service. Conclusions This study shows that tele-OT interventions can be an effective approach as part of the management of ADHD in children and that parents overall were satisfied with this approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".