Effectiveness of collaborative tele-mental health care for children with attention deficit hyperactivity disorder in United Arab Emirates
Bibliographic record
Abstract
Background: Attention deficit hyperactivity disorder is a common neurodevelopmental disorder. Accessing services for this disorder is a worldwide challenge and requires innovative interventions. Aims: We aimed to investigate the effectiveness of tele-collaborative care for attention deficit hyperactivity disorder in primary health care centres in Dubai. Methods: Six trained physicians started collaborative care clinics across Dubai. Eligible children aged 6-12 years attending primary health care centres with attention deficit hyperactivity disorder were randomly selected to receive telehealth collaborative care, or standard treatment. Baseline assessments were conducted using the Vanderbilt Behavioral Assessment Scale, the Columbia Impairment Scale, the Childhood Behavior Checklist, and the Strength and Difficulties Questionnaire. Waiting times and clinical and functional outcomes were measured in both groups and compared. Continuous variables were presented as means and standard deviations, categorical variables such as sex were presented as numbers and percentages, and continuous outcome variables were compared using the Student t-test. Results: Among the referred children (n = 112), 11 boys and 6 girls met the eligibility criteria (mean age 7.8 years). The dropout rate at 6 months in the control group was 80%, compared with 50% in the intervention group. The mean waiting time was significantly shorter in the intervention group (1.3 weeks) than the control group (7.1 weeks); P = 0.026. The mean difference in the Childhood Behavior Checklist total score over time was significantly higher in the intervention group (P = 0.042), but the mean difference in the Vanderbilt scale was not significant. Conclusion: Tele-collaborative care for children with attention deficit hyperactivity disorder within primary health care is feasible.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".