A Multimodal Intervention for Children with ADHD Reduces Inequity in Health and Education Outcomes
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether a multimodal intervention for children with attention-deficit hyperactivity disorder (ADHD) resulted in better long-term health and education outcomes and reduced inequity across the socioeconomic gradient. METHOD: We analyzed administrative data held in the Manitoba Population Research Data Repository describing recipients of a combined pharmacological/behavioural intervention for ADHD. The study cohort included children aged 5 to 17 years who visited the Manitoba Adolescent Treatment Centre's ADHD intervention service at least 3 times (2007-2012). Controls were matched on age, sex, year of ADHD diagnosis, and income quintile. We compared rates of hospital and emergency department visits, medication use and adherence, contact with child welfare services, and whether children were in their age-appropriate grade. We used concentration curves to estimate differences in outcomes between children from high- and low-income families. RESULTS: Children who received the intervention ( n = 485) had higher rates of medication use (rate ratio [RR], 1.21; 95% CI, 1.08 to 1.36) and adherence (RR, 1.42; 95% CI, 1.03 to 1.96) and were more likely to be in their age-appropriate grade (RR, 1.33; 95% CI, 1.09 to 1.63) compared with controls ( n = 1884). The intervention was also associated with reduced inequity in these outcomes across income deciles. There was no difference in the rates of hospital or emergency department visits or contacts with child welfare services. CONCLUSIONS: A multimodal ADHD intervention was associated with increased medication use and adherence and higher academic achievement. It was also related to lower inequity across the socioeconomic gradient. These results suggest that multimodal approaches may provide more equitable health and education outcomes for children with ADHD.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".