Increases in Behavioral Health Screening in Pediatric Care for Massachusetts Medicaid Patients
Bibliographic record
Abstract
OBJECTIVE: To explore rates of screening and identification and treatment for behavioral problems using billing data from Massachusetts Medicaid immediately following the start of the state's new court-ordered screening and intervention program. DESIGN: Retrospective review of the number of pediatric well-child visits, number of screens, and number of screens that identify risk for psychosocial problems from January 2008 (the month pediatric screening started) to March 2009. During the surrounding 1-year period, we also examined the number of claims with a behavioral health evaluation code. SETTING: Massachusetts. PARTICIPANTS: Massachusetts Medicaid-enrolled children. INTERVENTION: Funded court-ordered mandate to screen for mental health during Medicaid well-child visits. OUTCOME MEASURES: Percentage of visits with a screen, percentage of screens identified at risk, and number of children seen for behavioral health evaluations. RESULTS: Major increase from 16.6% of all Medicaid well-child visits coded for behavioral screens in the first quarter of 2008 to 53.6% in the first quarter of 2009. Additionally, the children identified as at risk increased substantially from about 1600 in the first quarter of 2008 to nearly 5000 in quarter 1 of 2009. The children with mental health evaluations increased from an average of 4543 to 5715 per month over a 1-year period. CONCLUSIONS: The data suggest payment and a supported mandate for use of a formal screening tool can substantially increase the identification of children at behavioral health risk. Findings suggest that increased screening may have the desired effect of increasing referrals for mental health services.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".