P01-260 - Population-based Comparison of Specialized and Community Based ADHD Service Rates: Evidence for a New Delivery Model
Bibliographic record
Abstract
Introduction Population-based analysis of regional and provincial data has permitted identification of areas for modeling innovative service delivery paradigms. Attention Deficit and Hyperactivity Disorder (ADHD) serves as an example. Within the same catchment region, comparing tertiary service utilization with provincial ADHD rates, we have identified a potential service gap. Potential to improve capacity based on a fiscally neutral model is discussed. Method Annual data collected in the regional child and adolescent mental health program information system from 2002-2009 was used to describe characteristics of those referred with a provisional diagnosis of ADHD. Regional population-based utilization rates were compared to the region-adjusted provincial rates of ADHD. Results As is typical, receiving a provisionally diagnosis of ADHD was significantly associated with longer wait for service and length of stay, being male and younger, greater comorbidity (e.g., conduct disorder), more behavioral problems and more problems at school. About half of the referrals came from community-based primary care physicians. Many children are diagnosed with ADHD by community physicians and relatively few receive specialized treatment. Conclusions Analysis of population-based service rates identify potentially large knowledge gap. Within this gap, the quality of service and fidelity to evidence-based practice in community-based treatment of ADHD are unknown. Community-based primary care practitioners have little specialized mental health training and may require support in delivering evidence-based care. To this end, we describe a model to address the identified service gap for ADHD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.097 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".