Impact of a Web-Portal Intervention on Community ADHD Care and Outcomes
Bibliographic record
Abstract
BACKGROUND: The quality of care for children with attention-deficit/hyperactivity disorder (ADHD) delivered in community-based pediatric settings is often poor. Interventions have been developed to improve community-based ADHD care but have not demonstrated that better care results in improved patient outcomes. The objective of this study was to determine whether an ADHD quality improvement (QI) intervention for community-based pediatric practices improves patient outcomes. METHODS: A cluster randomized controlled trial was conducted in which 50 community-based pediatric primary care practices (213 providers) were randomized either to receive a technology-assisted QI intervention or to a control condition. The intervention consisted of 4 training sessions, office flow modification, guided QI, and an ADHD Internet portal to assist with treatment monitoring. ADHD treatment processes and parent- and teacher-rated ADHD symptoms over the first year of treatment were collected for 577 patients. RESULTS: Intent-to-treat analyses examining outcomes of all children assessed for ADHD were not significant (b = -1.97, P = .08). However, among the 373 children prescribed ADHD medication, there was a significant intervention effect (b = -2.42, P = .04) indicating greater reductions in parent ratings of ADHD symptoms after treatment among patients treated by intervention physicians compared with patients treated at control practices. There were no group differences on teacher ratings of ADHD symptoms. ADHD treatment care around medication was significantly better at intervention practices compared with control practices. CONCLUSIONS: A technology-assisted QI intervention improved some ADHD care quality and resulted in additional reductions in parent-rated ADHD symptoms among patients prescribed ADHD medications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".