Enhancing Developmentally Oriented Primary Care: An Illinois Initiative to Increase Developmental Screening in Medical Homes
Bibliographic record
Abstract
In 2005, the Enhancing Developmentally Oriented Primary Care (EDOPC) project of the Illinois chapter of the American Academy of Pediatrics and the Illinois Department of Healthcare and Family Services began a project to improve the delivery and financing of preventive health and developmental services for children in Illinois. The leaders of this initiative sought to increase primary care providers' use of validated tools for developmental, social/emotional, maternal depression, and domestic violence screening and to increase early awareness of autism symptoms during pediatric well-child visits in children aged 0 to 3 years. These screenings facilitate identification of children at risk and those who need referral for further evaluation. Primary barriers to such screenings include lack of practitioner confidence in using validated screening tools. In this article we describe the accomplishments of the EDOPC project, which created training programs to address these barriers. This training is delivered by EDOPC staff and peer educators (physicians and nurse practitioners) in medical practices. The EDOPC project enhanced confidence and intent to screen among a large group of Illinois primary health care providers. Among a sample of primary care sites at which chart reviews were conducted, the EDOPC project increased developmental screening rates to the target of 85% of patients at most sites and increased social/emotional screening rates to the same target rate in nearly half of the participating practices.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".